AI Evaluation Engineer

at Iungotech

AI Evaluation Engineer

Iungotech

Company

Home

AI Evaluation Engineer at Iungotech · Remote only · 1 - 10 years · ₹10L - ₹70L / yr · Remote only · Posted 29 Oct 2026

AI Evaluation Engineer

at Iungotech

Posted by Philip Luther

1 - 10 yrs

₹10L - ₹70L / yr

Remote only

Skills

Python

Artificial Intelligence (AI)

We are looking for an AI Evaluation Engineer to evaluate, test, and improve the quality, reliability, and performance of large language models (LLMs) and generative AI applications.

You will design evaluation datasets, develop automated testing pipelines, analyze model outputs, and build metrics and tools to measure AI system performance across real-world use cases.

The ideal candidate has strong Python programming skills, an analytical mindset, and an interest in LLM evaluation, AI training, and data-driven model improvement.

Key Responsibilities

Design, develop, and maintain evaluation datasets and test cases for LLMs and AI-powered applications.
Evaluate model outputs for factual accuracy, relevance, reasoning, instruction following, consistency, and safety.

Required Qualifications

2+ years of experience in software development, data science, machine learning, or a related technical field.
Strong proficiency in Python and experience with data-processing libraries such as pandas and NumPy.

We are looking for an AI Evaluation Engineer to evaluate, test, and improve the quality, reliability, and performance of large language models (LLMs) and generative AI applications.

You will design evaluation datasets, develop automated testing pipelines, analyze model outputs, and build metrics and tools to measure AI system performance across real-world use cases.

The ideal candidate has strong Python programming skills, an analytical mindset, and an interest in LLM evaluation, AI training, and data-driven model improvement.

Key Responsibilities

Design, develop, and maintain evaluation datasets and test cases for LLMs and AI-powered applications.
Evaluate model outputs for factual accuracy, relevance, reasoning, instruction following, consistency, and safety.

Required Qualifications

2+ years of experience in software development, data science, machine learning, or a related technical field.
Strong proficiency in Python and experience with data-processing libraries such as pandas and NumPy.

Users love Cutshort

Read about what our users have to say about finding their next opportunity on Cutshort.

Shubham Vishwakarma

Full Stack Developer - Averlon

I had an amazing experience. It was a delight getting interviewed via Cutshort. The entire end to end process was amazing. I would like to mention Reshika, she was just amazing wrt guiding me through the process. Thank you team.

Companies hiring on Cutshort

About Iungotech

Founded

Type :

Services

Size :

0-20

Stage

About

IUNGO Technology is a global AI and software engineering team delivering advanced AI solutions, full-stack applications, enterprise platforms and immersive 3D experiences for businesses worldwide.

Company social profiles

N/A

Similar jobs (10)

Data Annotation

at Thomasmount Consulting

Posted by Shirin Shahana

Kochi (Cochin)

5 - 9 yrs

₹20L - ₹22L / yr

Data annotation

data labelling

Artificial Intelligence (AI)

Role Overview

We are looking for an experienced professional to evaluate AI-generated responses for conversational AI agents used in travel-related products. The role focuses on assessing response accuracy, relevance, completeness, and quality to improve AI performance.

Key Responsibilities

Evaluate AI-generated responses against predefined guidelines and quality standards.
Perform data annotation, labeling, classification, and quality reviews.
Identify incorrect, misleading, irrelevant, or incomplete responses and potential AI hallucinations.
Analyze evaluation data to identify recurring errors, quality gaps, and improvement opportunities.
Track quality metrics, document findings, and recommend improvements.
Use Excel/Google Sheets and basic SQL, where applicable, to analyze evaluation data.
Collaborate with AI and Machine Learning teams to improve conversational AI performance.

Required Skills

5–8 years of experience in Data Annotation, AI Evaluation, Content Quality, Search Relevance, or Quality Assurance.
Strong analytical, problem-solving, and attention-to-detail skills.
Experience applying evaluation guidelines and quality standards consistently.
Proficiency in Excel or Google Sheets.
Good written communication and documentation skills.
Understanding of quality metrics and data analysis.

Good to Have

Exposure to Generative AI, LLMs, conversational AI, or AI response evaluation.
Experience with Human-in-the-Loop (HITL), AI training data validation, or hallucination detection.
Basic SQL knowledge.
Experience in travel, hospitality, or search quality evaluation.

Role Overview

Key Responsibilities

Evaluate AI-generated responses against predefined guidelines and quality standards.
Perform data annotation, labeling, classification, and quality reviews.
Identify incorrect, misleading, irrelevant, or incomplete responses and potential AI hallucinations.
Analyze evaluation data to identify recurring errors, quality gaps, and improvement opportunities.
Track quality metrics, document findings, and recommend improvements.
Use Excel/Google Sheets and basic SQL, where applicable, to analyze evaluation data.
Collaborate with AI and Machine Learning teams to improve conversational AI performance.

Required Skills

5–8 years of experience in Data Annotation, AI Evaluation, Content Quality, Search Relevance, or Quality Assurance.
Strong analytical, problem-solving, and attention-to-detail skills.
Experience applying evaluation guidelines and quality standards consistently.
Proficiency in Excel or Google Sheets.
Good written communication and documentation skills.
Understanding of quality metrics and data analysis.

Good to Have

Exposure to Generative AI, LLMs, conversational AI, or AI response evaluation.
Experience with Human-in-the-Loop (HITL), AI training data validation, or hallucination detection.
Basic SQL knowledge.
Experience in travel, hospitality, or search quality evaluation.

LLM/AI Model Engineer – Training, Evaluation & Benchmarking

at QubeLabs Systems Private Limited

Posted by Nitish Kumar

Delhi

3 - 5 yrs

₹10L - ₹20L / yr

LLM Evaluation Frameworks

Fine-tuning LLMs

Open-source LLMs

Python

PyTorch

+4 more

LLM/AI Model Engineer – Training, Evaluation & Benchmarking

Location: New Delhi, India (On-site)

Employment Type: Full-time

Experience: 3–5 years

Industry: Enterprise AI / Generative AI / Machine Learning / NLP

About QubeLabs

QubeLabs is building the next generation of Enterprise AI Systems that transform workforce operations and intelligence for the financial services industry.

Our platform combines Conversational AI, Agentic AI, workflow automation, proprietary language models and enterprise intelligence. Our flagship product, QubeLabs Workmate, serves banks, NBFCs, MFIs, wealth management firms, insurance companies and fintechs across India and Europe.

At the core of our AI platform is the Vectro Series, our proprietary family of language models designed for enterprise and financial-services use cases.

We are looking for an LLM/AI Model Engineer to build the training pipelines, datasets and evaluation infrastructure required to continuously improve the Vectro Series.

Role Overview

You will work closely with the Head of AI, Research Partners and engineering teams to implement and operationalize LLM training approaches and build a reliable model improvement lifecycle:

Data Preparation → Training → Evaluation → Benchmarking → Error Analysis → Model Improvement

The role combines hands-on LLM fine-tuning, dataset engineering, tokenization, evaluation framework development and model quality management.

Key Responsibilities

1. LLM Training & Fine-tuning

Build and maintain pipelines for training and fine-tuning open-source foundation models for the Vectro Series.

Implement supervised fine-tuning (SFT), parameter-efficient fine-tuning (PEFT), LoRA/QLoRA and domain adaptation techniques; support continued pre-training where required.

Manage training configurations, checkpoints, model versions and experiment tracking.

Optimize training workflows for computational efficiency, reproducibility and reliability.

Translate model development approaches defined with the Head of AI and Research Partners into practical, scalable training pipelines.

2. Training Data & Tokenization

Build data pipelines to collect, clean, filter, normalize, deduplicate and validate training data.

Develop instruction-tuning, SFT and domain-specific datasets for financial-services and enterprise use cases.

Implement tokenization workflows, tokenizer configuration, vocabulary management, sequence packing, truncation and padding.

Address data representation challenges involving financial terminology, numerical values and multilingual content.

Maintain dataset versioning, lineage and quality controls, incorporating identified data gaps and relevant model failures into future training data.

3. Golden Dataset & Model Evaluation

Build and maintain QubeLabs' Golden Dataset to evaluate key model capabilities and target use cases.

Develop automated evaluation pipelines and reusable evaluation harnesses, combining automated metrics with human assessment where appropriate.

Evaluate accuracy, reasoning, factuality, instruction following, safety, multilingual performance and financial-domain capabilities.

Establish consistent evaluation methodology and regression tests to measure changes across model versions.

4. Benchmarking & Error Analysis

Evaluate Vectro against relevant public benchmarks and proprietary QubeLabs BFSI/enterprise benchmarks.

Compare results with relevant open-source and commercial models using consistent evaluation conditions.

Analyze failures, identify root causes across data, training and model behavior, and translate findings into actionable improvements.

Maintain reproducible benchmark results and reports to track model strengths, limitations and progress.

5. Model Quality & Release

Define model release quality gates and ensure each major release meets agreed evaluation and benchmark criteria.

Support red-teaming, robustness and safety testing.

Coordinate with the Head of AI, Research Partners and engineering teams to ensure validated model improvements are suitable for integration into production systems.

Experience

3–5 years in Machine Learning, Deep Learning, NLP, Generative AI or related fields.

Hands-on experience training or fine-tuning LLMs using open-source foundation models.

Practical experience building training pipelines, preparing datasets and evaluating model performance.

Experience with GPU-based training; distributed training is an advantage.

Financial-services, multilingual AI or enterprise AI experience is preferred.

Required Skills

LLM & Machine Learning: Large Language Models, Transformer architectures, Generative AI, NLP, Deep Learning, SFT, PEFT, LoRA/QLoRA, fine-tuning and continued pre-training.

Frameworks & Infrastructure: Python, PyTorch, Hugging Face Transformers, Hugging Face Tokenizers, GPU computing, experiment tracking and ML pipelines.

Data & Tokenization: Tokenization, tokenizer configuration, text preprocessing, sequence packing, dataset construction, data quality, versioning and lineage.

Evaluation & Benchmarking: Golden datasets, LLM evaluation, evaluation harnesses, automated and human evaluation, LLM-as-a-Judge, public and domain-specific benchmarks, regression testing and error analysis.

The role requires the ability to build both the model training pipeline and the measurement system that determines whether the model has actually improved.

What Success Looks Like

Reliable and reproducible training and fine-tuning pipelines for the Vectro Series.

High-quality training datasets, tokenization workflows and a comprehensive Golden Dataset.

Automated evaluation and benchmarking infrastructure with measurable model quality gates.

Clear, reproducible evidence of model performance against public, proprietary BFSI and relevant competing-model benchmarks.

A continuous improvement loop that converts evaluation findings into measurable gains across successive Vectro releases.

Key Performance Indicators

Training pipeline reliability and experiment throughput.

Training data quality and coverage.

Golden Dataset and evaluation coverage.

Benchmark reproducibility and model performance improvement.

Accuracy of error diagnosis and effectiveness of regression detection.

Time required to complete training, evaluation and benchmarking cycles.

Compliance with model release quality criteria.

Why Join QubeLabs?

Build the proprietary Vectro Series of enterprise and financial-services language models.

Develop model training, evaluation and benchmarking infrastructure from the ground up.

Work closely with the Head of AI and Research Partners.

Solve real-world AI challenges across financial services.

Develop proprietary datasets, benchmarks and model improvement systems.

Contribute directly to production AI systems serving customers across India and Europe.

Preferred Candidate Profile

B.Tech, M.Tech, MS or PhD in Computer Science, AI, ML, Mathematics or a related field.

3–5 years of relevant experience with strong practical skills in Python, PyTorch, LLM fine-tuning and training data pipelines.

Working knowledge of tokenization, evaluation datasets, benchmarking and regression testing.

Experience with open-source models such as Llama, Qwen, Mistral or similar is preferred.

Ability to independently build reliable model training and evaluation systems from scratch.

QubeLabs is an equal opportunity employer and values diversity and inclusion. We encourage applications from qualified candidates regardless of background or identity.

LLM/AI Model Engineer – Training, Evaluation & Benchmarking

Location: New Delhi, India (On-site)

Employment Type: Full-time

Experience: 3–5 years

Industry: Enterprise AI / Generative AI / Machine Learning / NLP

About QubeLabs

QubeLabs is building the next generation of Enterprise AI Systems that transform workforce operations and intelligence for the financial services industry.

At the core of our AI platform is the Vectro Series, our proprietary family of language models designed for enterprise and financial-services use cases.

We are looking for an LLM/AI Model Engineer to build the training pipelines, datasets and evaluation infrastructure required to continuously improve the Vectro Series.

Role Overview

You will work closely with the Head of AI, Research Partners and engineering teams to implement and operationalize LLM training approaches and build a reliable model improvement lifecycle:

Data Preparation → Training → Evaluation → Benchmarking → Error Analysis → Model Improvement

The role combines hands-on LLM fine-tuning, dataset engineering, tokenization, evaluation framework development and model quality management.

Key Responsibilities

1. LLM Training & Fine-tuning

Build and maintain pipelines for training and fine-tuning open-source foundation models for the Vectro Series.

Implement supervised fine-tuning (SFT), parameter-efficient fine-tuning (PEFT), LoRA/QLoRA and domain adaptation techniques; support continued pre-training where required.

Manage training configurations, checkpoints, model versions and experiment tracking.

Optimize training workflows for computational efficiency, reproducibility and reliability.

Translate model development approaches defined with the Head of AI and Research Partners into practical, scalable training pipelines.

2. Training Data & Tokenization

Build data pipelines to collect, clean, filter, normalize, deduplicate and validate training data.

Develop instruction-tuning, SFT and domain-specific datasets for financial-services and enterprise use cases.

Implement tokenization workflows, tokenizer configuration, vocabulary management, sequence packing, truncation and padding.

Address data representation challenges involving financial terminology, numerical values and multilingual content.

Maintain dataset versioning, lineage and quality controls, incorporating identified data gaps and relevant model failures into future training data.

3. Golden Dataset & Model Evaluation

Build and maintain QubeLabs' Golden Dataset to evaluate key model capabilities and target use cases.

Develop automated evaluation pipelines and reusable evaluation harnesses, combining automated metrics with human assessment where appropriate.

Evaluate accuracy, reasoning, factuality, instruction following, safety, multilingual performance and financial-domain capabilities.

Establish consistent evaluation methodology and regression tests to measure changes across model versions.

4. Benchmarking & Error Analysis

Evaluate Vectro against relevant public benchmarks and proprietary QubeLabs BFSI/enterprise benchmarks.

Compare results with relevant open-source and commercial models using consistent evaluation conditions.

Analyze failures, identify root causes across data, training and model behavior, and translate findings into actionable improvements.

Maintain reproducible benchmark results and reports to track model strengths, limitations and progress.

5. Model Quality & Release

Define model release quality gates and ensure each major release meets agreed evaluation and benchmark criteria.

Support red-teaming, robustness and safety testing.

Coordinate with the Head of AI, Research Partners and engineering teams to ensure validated model improvements are suitable for integration into production systems.

Experience

3–5 years in Machine Learning, Deep Learning, NLP, Generative AI or related fields.

Hands-on experience training or fine-tuning LLMs using open-source foundation models.

Practical experience building training pipelines, preparing datasets and evaluating model performance.

Experience with GPU-based training; distributed training is an advantage.

Financial-services, multilingual AI or enterprise AI experience is preferred.

Required Skills

LLM & Machine Learning: Large Language Models, Transformer architectures, Generative AI, NLP, Deep Learning, SFT, PEFT, LoRA/QLoRA, fine-tuning and continued pre-training.

Frameworks & Infrastructure: Python, PyTorch, Hugging Face Transformers, Hugging Face Tokenizers, GPU computing, experiment tracking and ML pipelines.

Data & Tokenization: Tokenization, tokenizer configuration, text preprocessing, sequence packing, dataset construction, data quality, versioning and lineage.

The role requires the ability to build both the model training pipeline and the measurement system that determines whether the model has actually improved.

What Success Looks Like

Reliable and reproducible training and fine-tuning pipelines for the Vectro Series.

High-quality training datasets, tokenization workflows and a comprehensive Golden Dataset.

Automated evaluation and benchmarking infrastructure with measurable model quality gates.

Clear, reproducible evidence of model performance against public, proprietary BFSI and relevant competing-model benchmarks.

A continuous improvement loop that converts evaluation findings into measurable gains across successive Vectro releases.

Key Performance Indicators

Training pipeline reliability and experiment throughput.

Training data quality and coverage.

Golden Dataset and evaluation coverage.

Benchmark reproducibility and model performance improvement.

Accuracy of error diagnosis and effectiveness of regression detection.

Time required to complete training, evaluation and benchmarking cycles.

Compliance with model release quality criteria.

Why Join QubeLabs?

Build the proprietary Vectro Series of enterprise and financial-services language models.

Develop model training, evaluation and benchmarking infrastructure from the ground up.

Work closely with the Head of AI and Research Partners.

Solve real-world AI challenges across financial services.

Develop proprietary datasets, benchmarks and model improvement systems.

Contribute directly to production AI systems serving customers across India and Europe.

Preferred Candidate Profile

B.Tech, M.Tech, MS or PhD in Computer Science, AI, ML, Mathematics or a related field.

3–5 years of relevant experience with strong practical skills in Python, PyTorch, LLM fine-tuning and training data pipelines.

Working knowledge of tokenization, evaluation datasets, benchmarking and regression testing.

Experience with open-source models such as Llama, Qwen, Mistral or similar is preferred.

Ability to independently build reliable model training and evaluation systems from scratch.

QubeLabs is an equal opportunity employer and values diversity and inclusion. We encourage applications from qualified candidates regardless of background or identity.

AI Engineer – Agents, Vision & Document Intelligence

workwise

Agency job

via Hello Edge by Saquib Mundagnur

Bengaluru (Bangalore)

3 - 5 yrs

₹20L - ₹30L / yr

Large Language Models (LLM)

Python

Retrieval Augmented Generation (RAG)

RESTful APIs

Vector database

+3 more

Location -Bangalore

Work Mode -Fully in-office, 6 days a week

Experience -3+ years in AI engineering, with LLM-based systems shipped to production

About the Role

● The AI Engineer will own systems that read, search and reason over project and tender documents.

● Inputs include large tender packages, scanned PDFs, BOQs, tables, forms and engineering drawings such as P&IDs.; Outputs should include accurate answers with source references, structured data and workflows used by estimators and project teams in live bids, planning, procurement and execution.

● The role covers taking features from initial experiments to production and owning their accuracy, latency and cost after release.

Key Responsibilities

Agents and Retrieval

● Build AI agents that plan and execute multi-step tasks using document search, tools and APIs.

● Build RAG pipelines covering ingestion, retrieval, reranking and answers with source references.

● Use agent workflows within retrieval to refine searches, collect supporting evidence and verify outputs.

Document Intelligence

● Build extraction pipelines for PDFs, scanned documents, tables and forms.

● Convert unstructured tender content into structured data that the platform can use.

Vision and Image Understanding

● Build image-understanding workflows for P&IDs;, including symbols, equipment, labels and connections.

● Locate and remove selected elements from diagram images while preserving surrounding content.

Quality and Reliability

● Create evaluation datasets and automated checks to measure accuracy and catch regressions.

● Debug failures across retrieval, agent decisions, tool calls and model outputs.

Product and Production

● Build backend services and REST APIs. ● Integrate AI features into the Workwise web application.

● Take features from experiment to production while managing reliability, latency and cost

Required Qualifications

● 3+ years of hands-on experience building AI systems, including at least one LLM-based application running in production.

● Strong Python and software engineering fundamentals.

● Experience with backend services, REST APIs, databases, authentication and frontend integration.

● Experience building applications using LLM APIs.

● Working knowledge of RAG, embeddings, retrieval and vector databases.

● Experience with tool calling, structured outputs and multi-step agent workflows.

● Working understanding of multimodal models and image processing for visual analysis and editing.

● Systematic evaluation skills: measuring results and investigating system failures.

● Ownership of implementation, testing, deployment and ongoing improvement.

Good to Have

● OCR, document layout analysis or complex table extraction.

● Computer vision experience, including object detection, segmentation or image inpainting.

● Familiarity with construction, procurement or tender documents.

● Cloud infrastructure and production monitoring.

● Working knowledge of Go.

Location -Bangalore

Work Mode -Fully in-office, 6 days a week

Experience -3+ years in AI engineering, with LLM-based systems shipped to production

About the Role

● The AI Engineer will own systems that read, search and reason over project and tender documents.

● The role covers taking features from initial experiments to production and owning their accuracy, latency and cost after release.

Key Responsibilities

Agents and Retrieval

● Build AI agents that plan and execute multi-step tasks using document search, tools and APIs.

● Build RAG pipelines covering ingestion, retrieval, reranking and answers with source references.

● Use agent workflows within retrieval to refine searches, collect supporting evidence and verify outputs.

Document Intelligence

● Build extraction pipelines for PDFs, scanned documents, tables and forms.

● Convert unstructured tender content into structured data that the platform can use.

Vision and Image Understanding

● Build image-understanding workflows for P&IDs;, including symbols, equipment, labels and connections.

● Locate and remove selected elements from diagram images while preserving surrounding content.

Quality and Reliability

● Create evaluation datasets and automated checks to measure accuracy and catch regressions.

● Debug failures across retrieval, agent decisions, tool calls and model outputs.

Product and Production

● Build backend services and REST APIs. ● Integrate AI features into the Workwise web application.

● Take features from experiment to production while managing reliability, latency and cost

Required Qualifications

● 3+ years of hands-on experience building AI systems, including at least one LLM-based application running in production.

● Strong Python and software engineering fundamentals.

● Experience with backend services, REST APIs, databases, authentication and frontend integration.

● Experience building applications using LLM APIs.

● Working knowledge of RAG, embeddings, retrieval and vector databases.

● Experience with tool calling, structured outputs and multi-step agent workflows.

● Working understanding of multimodal models and image processing for visual analysis and editing.

● Systematic evaluation skills: measuring results and investigating system failures.

● Ownership of implementation, testing, deployment and ongoing improvement.

Good to Have

● OCR, document layout analysis or complex table extraction.

● Computer vision experience, including object detection, segmentation or image inpainting.

● Familiarity with construction, procurement or tender documents.

● Cloud infrastructure and production monitoring.

● Working knowledge of Go.

Associate Lead - AI/ML Engineer ( Python, AL/ML Tier 1 college only

at Staffnixcom

Posted by Mayank Choudhary

Pune

3 - 6 yrs

₹27L - ₹32L / yr

Artificial Intelligence (AI)

Strong AI Engineer / Machine Learning Engineer profiles.

Mandatory (Experience 1) – Must have minimum 3+ years of hands-on experience in Data Science, Machine Learning, Applied AI, NLP, Deep Learning, or Generative AI solutions.

Mandatory (Experience 2) – Must have strong hands-on experience in Python programming, SQL, data analysis, feature engineering, model development, and production-grade ML applications.

Mandatory (Experience 3) – Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent.

Mandatory (Experience 4) – Must have hands-on experience working on NLP, embeddings, semantic search, text classification, document understanding, recommendation systems, or similar AI/ML use cases.

Mandatory (Experience 5) – Must have experience working with Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, Phi, or similar foundation models.

Mandatory (Experience 6) – Must have hands-on experience building or implementing RAG (Retrieval Augmented Generation) systems, vector search, knowledge retrieval, embeddings, chunking, indexing, or semantic retrieval solutions.

Mandatory (Experience 7) – Must have experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems.

Mandatory ( Pedigree) - B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are Considered.

Strong AI Engineer / Machine Learning Engineer profiles.

Mandatory (Experience 1) – Must have minimum 3+ years of hands-on experience in Data Science, Machine Learning, Applied AI, NLP, Deep Learning, or Generative AI solutions.

Mandatory (Experience 2) – Must have strong hands-on experience in Python programming, SQL, data analysis, feature engineering, model development, and production-grade ML applications.

Mandatory (Experience 3) – Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent.

Mandatory (Experience 5) – Must have experience working with Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, Phi, or similar foundation models.

Mandatory (Experience 7) – Must have experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems.

Mandatory ( Pedigree) - B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are Considered.

Senior AI/ML Engineer (LLM, Python)

at Staffnixcom

Posted by Mayank Choudhary

Hyderabad

3 - 6 yrs

₹26L - ₹28L / yr

Large Language Models (LLM) tuning

Strong Senior AI Engineer Profile with production-grade LLM and Python expertise

Mandatory (Experience 1): Must have at least 3+ years of professional AI/ML engineering experience with demonstrated track record of delivering production-grade AI systems in real-world environments

Mandatory (Tech skill 1): Must have strong programming skills in Python and SQL, with hands-on ML/data libraries (scikit-learn, pandas, numpy) and deep-learning frameworks (PyTorch or TensorFlow), plus REST API design

Mandatory (Experience 2): Must have hands-on experience building and deploying production-grade ML/LLMs including RAG pipelines, document parsing, information extraction, and text processing on large-scale unstructured data (preprocessing, chunking, embeddings, feature engineering)

Mandatory (Tech skill 2): Must have strong NLP / extraction-focused ML depth — transformers, embeddings, vector databases, RAG, LLM integrations, and agentic workflows.

Mandatory (Tech skill 3): Must have hands-on experience with AWS (SageMaker, Bedrock, EC2, Lambda) and Azure (AI Foundry, Azure OpenAI) for model training, fine-tuning, deployment, and inference at scale

Mandatory (Tech skill 4): Must have experience with multi-agentic frameworks / orchestration tools (Claude Code, LangGraph, LangChain, CrewAI)

Mandatory (Tech skill 5): Must have hands-on experience with MLOps ecosystem including experiment tracking (MLflow, Weights & Biases), model versioning/registry, automated retraining, and CI/CD (GitHub Actions, Azure DevOps, Docker, Kubernetes)

Mandatory (Tech skill 6): Must have experience with evaluation frameworks (precision, recall, F1, field-level accuracy) and iterative model improvement, plus AI observability (Prometheus, Grafana, SLOs).

Mandatory (Team Leadership): Must have experience leading projects or teams, managing technical deliverables, and mentoring, with strong problem-solving in ambiguous challenges

Mandatory (Education) - B.Tech or B.E. If graduated in BCA or BCS, they should have a Maters or Post graduation

Mandatory (Company): B2B Tech/Consulting/B2B Software

Strong Senior AI Engineer Profile with production-grade LLM and Python expertise

Mandatory (Experience 1): Must have at least 3+ years of professional AI/ML engineering experience with demonstrated track record of delivering production-grade AI systems in real-world environments

Mandatory (Tech skill 2): Must have strong NLP / extraction-focused ML depth — transformers, embeddings, vector databases, RAG, LLM integrations, and agentic workflows.

Mandatory (Tech skill 4): Must have experience with multi-agentic frameworks / orchestration tools (Claude Code, LangGraph, LangChain, CrewAI)

Mandatory (Team Leadership): Must have experience leading projects or teams, managing technical deliverables, and mentoring, with strong problem-solving in ambiguous challenges

Mandatory (Education) - B.Tech or B.E. If graduated in BCA or BCS, they should have a Maters or Post graduation

Mandatory (Company): B2B Tech/Consulting/B2B Software

Senior AI/ML Engineer (LLM, Python) immediate joiner only

at Staffnixcom

Posted by Mayank Choudhary

Hyderabad , Indore, Ahmedabad

3 - 5 yrs

₹21L - ₹27L / yr

LLM

Strong Senior AI Engineer Profile with production-grade LLM and Python expertise

Mandatory (Experience 1): Must have at least 3+ years of professional AI/ML engineering experience with demonstrated track record of delivering production-grade AI systems in real-world environments

Mandatory (Tech skill 2): Must have strong NLP / extraction-focused ML depth — transformers, embeddings, vector databases, RAG, LLM integrations, and agentic workflows.

Mandatory (Tech skill 4): Must have experience with multi-agentic frameworks / orchestration tools (Claude Code, LangGraph, LangChain, CrewAI)

Mandatory (Team Leadership): Must have experience leading projects or teams, managing technical deliverables, and mentoring, with strong problem-solving in ambiguous challenges

Mandatory (Education) - B.Tech or B.E. If graduated in BCA or BCS, they should have a Maters or Post graduation

Mandatory (Company): B2B Tech/Consulting/B2B Software

Strong Senior AI Engineer Profile with production-grade LLM and Python expertise

Mandatory (Experience 1): Must have at least 3+ years of professional AI/ML engineering experience with demonstrated track record of delivering production-grade AI systems in real-world environments

Mandatory (Tech skill 2): Must have strong NLP / extraction-focused ML depth — transformers, embeddings, vector databases, RAG, LLM integrations, and agentic workflows.

Mandatory (Tech skill 4): Must have experience with multi-agentic frameworks / orchestration tools (Claude Code, LangGraph, LangChain, CrewAI)

Mandatory (Team Leadership): Must have experience leading projects or teams, managing technical deliverables, and mentoring, with strong problem-solving in ambiguous challenges

Mandatory (Education) - B.Tech or B.E. If graduated in BCA or BCS, they should have a Maters or Post graduation

Mandatory (Company): B2B Tech/Consulting/B2B Software

Product Engineer

at Prentis

Posted by Anika fromTitanHolding

Bengaluru (Bangalore)

3 - 5 yrs

₹25L - ₹30L / yr

Large Language Models (LLM)

AI Agents

Python

Retrieval Augmented Generation (RAG)

REST APIs

+3 more

Product Engineer — Role Summary

We are looking for a Product Engineer to build user-facing products that combine AI capabilities with practical applications. You will work at the intersection of software engineering and AI, developing autonomous, agent-driven systems that solve complex educational and research problems.

Key Responsibilities:

Product Development: Design, build, and deploy production-ready applications powered by LLMs and AI agents.
Data Engineering: Build scalable ETL/ELT pipelines to process structured and unstructured data, including text and audio, for RAG and model fine-tuning.
Agentic Workflows: Develop multi-step AI agents with tool calling, APIs, databases, search, reasoning, and memory.
Rapid Prototyping: Turn ideas and research concepts into interactive, production-ready applications.
AI Integration: Use frameworks such as LangChain, LlamaIndex, AutoGen, or custom orchestrators to integrate AI into scalable systems.
User Experience: Transform raw AI outputs into reliable, intuitive, and responsive user experiences.
Collaboration: Work closely with ML researchers and data engineers to integrate custom and fine-tuned models.
Observability: Monitor agent behavior, manage edge cases, reduce hallucinations, and improve reliability in production.

The ideal candidate combines strong software engineering, AI/LLM expertise, data engineering, and product thinking, with the ability to take an AI concept from prototype to production.

Product Engineer — Role Summary

Key Responsibilities:

Product Development: Design, build, and deploy production-ready applications powered by LLMs and AI agents.
Data Engineering: Build scalable ETL/ELT pipelines to process structured and unstructured data, including text and audio, for RAG and model fine-tuning.
Agentic Workflows: Develop multi-step AI agents with tool calling, APIs, databases, search, reasoning, and memory.
Rapid Prototyping: Turn ideas and research concepts into interactive, production-ready applications.
AI Integration: Use frameworks such as LangChain, LlamaIndex, AutoGen, or custom orchestrators to integrate AI into scalable systems.
User Experience: Transform raw AI outputs into reliable, intuitive, and responsive user experiences.
Collaboration: Work closely with ML researchers and data engineers to integrate custom and fine-tuned models.
Observability: Monitor agent behavior, manage edge cases, reduce hallucinations, and improve reliability in production.

The ideal candidate combines strong software engineering, AI/LLM expertise, data engineering, and product thinking, with the ability to take an AI concept from prototype to production.

Voice AI Research Engineer

at LeadSquared

8 recruiters

Agency job

via Right Hire by Vrishali Mishra

Bengaluru (Bangalore)

4 - 6 yrs

₹20L - ₹40L / yr

Python

Speech-to-Text (STT)

ASR

Text-to-Speech (TTS)

Large Language Models (LLM)

+3 more

About Us

Invorto is our Voice AI product, bringing intelligent voice agents to real-world customer and operational use cases. Our voice pipeline is built in Python, running an STT → LLM → TTS architecture on top of the Pipecat framework.

This is a chance to work on hard problems in voice AI — latency, accuracy, naturalness, and reliability — building zero-to-one, owning your area end-to-end, and shipping to production at scale.

Note: This is a customer-facing role, and strong communication skills are essential.

About the Role

We're looking for a Voice AI Research Engineer to join the Invorto team and help build and continuously improve the voice AI systems that power our intelligent voice agents. This role is focused on the specialized craft of voice AI — designing evaluation and automation frameworks that ensure our STT, LLM, and TTS pipeline performs reliably in real-world, production conditions.

What You'll Do

Design and build automated testing and quality frameworks for our STT → LLM → TTS voice pipeline, built on Pipecat
Evaluate and benchmark STT, LLM, and TTS/ASR components on accuracy, latency, naturalness, and robustness across accents, languages, and real-world audio conditions
Work hands-on with STT, TTS, and ASR models — fine-tuning, evaluating, and improving them for production use cases
Identify failure modes and edge cases across the pipeline (background noise, accents, interruptions, turn-taking, latency, pipeline-stage handoffs) and build systems to catch them before production
Collaborate closely with engineering to integrate quality checks and automation into the voice agent development lifecycle within the Pipecat-based architecture
Research and stay current with advances in voice AI, and bring in new techniques, models, and tools to improve pipeline performance
Work directly with customers to understand real-world voice use cases and translate them into evaluation criteria and quality benchmarks
Partner with product and engineering to define what "production-grade quality" means for voice agents and drive the team toward it

What We're Looking For

4–6 years of experience, with a specialization in voice AI systems and automated quality evaluation
Hands-on experience with STT (Speech-to-Text), TTS (Text-to-Speech), and ASR (Automatic Speech Recognition) models
Experience designing and building automated testing/evaluation frameworks for voice or speech systems
Strong understanding of what drives voice AI quality — accuracy, latency, naturalness, and robustness to real-world variability
Strong programming skills in Python; familiarity with Pipecat or similar voice pipeline/orchestration frameworks is a plus
Understanding of STT → LLM → TTS pipeline architectures and the trade-offs involved at each stage
Research mindset — comfortable exploring new models, techniques, and tools and translating them into practical improvements
Excellent communication skills — this is a customer-facing role, and you'll regularly engage directly with customers to understand needs and validate quality expectations

About Us

This is a chance to work on hard problems in voice AI — latency, accuracy, naturalness, and reliability — building zero-to-one, owning your area end-to-end, and shipping to production at scale.

Note: This is a customer-facing role, and strong communication skills are essential.

About the Role

What You'll Do

Design and build automated testing and quality frameworks for our STT → LLM → TTS voice pipeline, built on Pipecat
Evaluate and benchmark STT, LLM, and TTS/ASR components on accuracy, latency, naturalness, and robustness across accents, languages, and real-world audio conditions
Work hands-on with STT, TTS, and ASR models — fine-tuning, evaluating, and improving them for production use cases
Identify failure modes and edge cases across the pipeline (background noise, accents, interruptions, turn-taking, latency, pipeline-stage handoffs) and build systems to catch them before production
Collaborate closely with engineering to integrate quality checks and automation into the voice agent development lifecycle within the Pipecat-based architecture
Research and stay current with advances in voice AI, and bring in new techniques, models, and tools to improve pipeline performance
Work directly with customers to understand real-world voice use cases and translate them into evaluation criteria and quality benchmarks
Partner with product and engineering to define what "production-grade quality" means for voice agents and drive the team toward it

What We're Looking For

4–6 years of experience, with a specialization in voice AI systems and automated quality evaluation
Hands-on experience with STT (Speech-to-Text), TTS (Text-to-Speech), and ASR (Automatic Speech Recognition) models
Experience designing and building automated testing/evaluation frameworks for voice or speech systems
Strong understanding of what drives voice AI quality — accuracy, latency, naturalness, and robustness to real-world variability
Strong programming skills in Python; familiarity with Pipecat or similar voice pipeline/orchestration frameworks is a plus
Understanding of STT → LLM → TTS pipeline architectures and the trade-offs involved at each stage
Research mindset — comfortable exploring new models, techniques, and tools and translating them into practical improvements
Excellent communication skills — this is a customer-facing role, and you'll regularly engage directly with customers to understand needs and validate quality expectations

AI Engineer

AI Company

Agency job

via Vikash Technologies by Rishika Teja

Pune

5 - 8 yrs

₹15L - ₹28L / yr

Artificial Intelligence (AI)

Generative AI

Agentic AI

Retrieval Augmented Generation (RAG)

Large Language Models (LLM)

+1 more

Hiring for AI Engineer

Exp: 5 - 8 yrs

Edu : BE/B.Tech/MCA

Work Location : Pune

Skill Set:

Total experience ranging from 5 - 8 years in software engineering/AI roles

Min 5 years strong programming experience in Python or Typescript is a MUST

Min 2.5 years hands-on experience in AI with LLMs, RAG pipelines, and AI frameworks

Experience with cloud platforms (AWS/Azure/GCP)

Hiring for AI Engineer

Exp: 5 - 8 yrs

Edu : BE/B.Tech/MCA

Work Location : Pune

Skill Set:

Total experience ranging from 5 - 8 years in software engineering/AI roles

Min 5 years strong programming experience in Python or Typescript is a MUST

Min 2.5 years hands-on experience in AI with LLMs, RAG pipelines, and AI frameworks

Experience with cloud platforms (AWS/Azure/GCP)

Data Scientist / AI Engineer (Python, ML, RAG Tier 1 college only

at Staffnixcom

Posted by Mayank Choudhary

Pune

3 - 6 yrs

₹27L - ₹32L / yr

Artificial Intelligence (AI)

Strong Data Scientist / AI Engineer / Generative AI Engineer profile.

Mandatory (Experience 1) - Must have 3+ years of hands-on experience in Data Science, Artificial Intelligence, Machine Learning, Deep Learning, NLP, or Generative AI application development.

Mandatory (Experience 2) - Must have strong hands-on experience in Python programming, backend development, API development, and production-grade application support.

Mandatory (Experience 3) - Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, or Scikit-learn.

Mandatory (Experience 4) - Must have hands-on experience in NLP use cases such as text classification, sentiment analysis, entity recognition (NER), semantic search, embeddings, or document understanding.

Mandatory (Experience 5) - Must have experience working with Large Language Models (LLMs) such as GPT, LLaMA, Mistral, Phi, Claude, Gemini, or similar models.

Mandatory (Experience 6) - Must have hands-on experience building or implementing Retrieval Augmented Generation (RAG) solutions, vector search, semantic search, or knowledge-based AI applications.

Mandatory (Experience 7) - Must have experience with Prompt Engineering and Generative AI frameworks such as LangChain, LangGraph, AI Agents, Azure OpenAI, or similar technologies.

Mandatory (Experience 8) - Must have experience developing, consuming, or integrating APIs using Python frameworks such as FastAPI, Flask, or similar technologies.

Mandatory (Education) - B.TECH / M.TECH from Tier 1 Colleges (IIT, NIT, BITS, IIIT, DTU, NSUT) are considered.