Applied AI Engineer (AI/ML & LLM Deployment)

Support with design and build to prove out agentic AI solution flow by working with other data 

scientists and engineers to build, train Large Language Model (LLM) architectures, RAG 

systems, and autonomous agentic workflows 

Key qualifications: 

>> AI solution design & Development: Design Agentic AI solutions using RAG (Retrieval-

Augmented Generation) and orchestration frameworks like LangGraph or LangChain. 

>> Model Fine-Tuning: Solid understanding and experience with Pre-train, fine-tune, and 

optimize open-source like BERT, LLama, and other proprietary foundation models for domain-

specific tasks 

>> Solid Stats and ML foundations and (vibe) coding skills with Python, PySpark 

>>  Implement validation frameworks and tracing practices (using tools like Arize) to monitor 

agent behavior, guard against model drift, and ensure compliance 

>> Collaborate with Engineering to deploy models securely on cloud and on-prem ecosystems 

Job Description – Python & Generative AI Engineer

Python & Generative AI Engineer

Location: Bangalore, India

Experience: 4+ Years

Employment Type: Full-time

Job Summary

We are looking for an experienced Python & Generative AI Engineer with 4+ years of software development experience and strong hands-on expertise in Python, LLMs, Generative AI, and AI application development.

The ideal candidate should be comfortable building production-grade AI solutions, integrating LLMs with enterprise applications, and developing scalable APIs and services using Python.

Key Responsibilities

• Design, develop, and deploy Generative AI applications using Python and modern AI/ML frameworks.
• Work with Large Language Models (LLMs) such as GPT, Claude, Gemini, Llama, or similar models.
• Develop RAG (Retrieval-Augmented Generation) pipelines using vector databases and embedding models.
• Build AI-powered applications using frameworks such as LangChain, LangGraph, LlamaIndex, or equivalent.
• Develop scalable REST APIs and backend services using FastAPI, Flask, or Django.
• Integrate LLM APIs, prompt engineering, function/tool calling, and structured outputs into enterprise applications.
• Work with vector databases such as FAISS, Pinecone, Weaviate, Milvus, or Azure AI Search.
• Implement document processing, chunking, embeddings, semantic search, and knowledge retrieval solutions.
• Evaluate LLM responses for accuracy, relevance, hallucination, latency, and cost.
• Build and maintain production-ready AI pipelines with appropriate monitoring, logging, security, and error handling.
• Collaborate with data scientists, ML engineers, software engineers, and business stakeholders to deliver AI solutions.
• Write clean, reusable, testable, and well-documented Python code.
• Participate in architecture discussions, code reviews, testing, deployment, and production support.

Required Skills

• 4+ years of experience in software/Python development.
• Strong proficiency in Python and object-oriented programming.
• Hands-on experience with Generative AI and LLM-based applications.
• Experience with OpenAI/Azure OpenAI, Anthropic, Google Gemini, or open-source LLMs.
• Strong understanding of Prompt Engineering and LLM application patterns.
• Experience implementing RAG pipelines.
• Knowledge of embeddings, vector databases, semantic search, and document retrieval.
• Experience with LangChain, LangGraph, LlamaIndex, or similar frameworks.
• Strong experience developing REST APIs using FastAPI/Flask/Django.
• Familiarity with Git, CI/CD, Docker, and cloud platforms such as AWS, Azure, or GCP.
• Good understanding of SQL and databases.
• Strong problem-solving and communication skills.

Good to Have

• Experience with AI agents / Agentic AI and tool/function calling.
• Experience with multi-agent frameworks.
• Knowledge of MLOps/LLMOps and model evaluation.
• Experience with Kubernetes and containerized deployments.
• Knowledge of NLP, machine learning, or deep learning.
• Experience with Azure AI, AWS Bedrock, Amazon SageMaker, or Google Vertex AI.
• Understanding of responsible AI, data privacy, and LLM security.

Education

Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related field.

Job Summary

We are looking for an experienced AI/ML Engineer to design, develop, deploy, and maintain machine learning and AI solutions that address complex business problems. The ideal candidate should have strong hands-on experience in Python, Machine Learning, Generative AI, LLMs, and AI/ML deployment, with the ability to work across the complete AI/ML lifecycle.

Key Responsibilities

• Design, develop, and deploy scalable Machine Learning and AI models for real-world business use cases.
• Build and optimize ML pipelines covering data preparation, feature engineering, model development, evaluation, and deployment.
• Develop solutions using Generative AI, Large Language Models (LLMs), NLP, and deep learning.
• Work with LLMs, prompt engineering, embeddings, vector databases, and Retrieval-Augmented Generation (RAG) architectures.
• Integrate AI/ML models with enterprise applications and APIs.
• Fine-tune and evaluate ML/LLM models based on business requirements.
• Implement MLOps practices for model versioning, deployment, monitoring, and continuous improvement.
• Collaborate with Data Scientists, Software Engineers, Architects, Product Managers, and business stakeholders.
• Conduct model performance evaluation, optimization, and troubleshooting.
• Ensure AI solutions meet requirements around security, scalability, reliability, responsible AI, and data privacy.
• Stay current with emerging AI/ML technologies, frameworks, and industry best practices.

Required Skills

• Strong programming experience in Python.
• Strong understanding of Machine Learning algorithms, statistics, and data structures.
• Hands-on experience with ML frameworks such as TensorFlow, PyTorch, Scikit-learn, or equivalent.
• Experience with Generative AI and LLMs such as OpenAI, Azure OpenAI, Claude, Gemini, or open-source models.
• Strong knowledge of Prompt Engineering, RAG, embeddings, vector databases, and AI agents.
• Experience with NLP, deep learning, or computer vision is an advantage.
• Experience developing and consuming REST APIs and microservices.
• Working knowledge of SQL and NoSQL databases.
• Experience with cloud platforms such as Azure, AWS, or GCP.
• Understanding of Docker, Kubernetes, CI/CD, and MLOps.
• Familiarity with Git and modern software development practices.

Preferred Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
• 4 years of relevant experience in AI/ML engineering or a related field.
• Experience building and deploying production-grade AI/ML solutions.
• Enterprise application development experience.
• Experience with Azure OpenAI, AWS Bedrock, Vertex AI, or similar managed AI platforms.
• Experience with LangChain, LlamaIndex, Semantic Kernel, or comparable AI frameworks is a plus.
• Experience with AI/ML model monitoring, evaluation, and optimization.

What You Bring

• Strong problem-solving and analytical skills.
• Ability to translate business requirements into practical AI/ML solutions.
• Strong software engineering and debugging capabilities.
• Ability to work independently as well as collaboratively in a cross-functional environment.
• Good communication skills with the ability to explain complex AI concepts to technical and non-technical stakeholders.

Keywords

AI Engineer | ML Engineer | Machine Learning | Generative AI | LLM | Python | NLP | Deep Learning | RAG | Prompt Engineering | AI Agents | Azure OpenAI | AWS Bedrock | MLOps | TensorFlow | PyTorch | Scikit-learn | Vector Database | Cloud AI

Location: Gurugram

Work mode: Hybrid

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.

About the role

We are seeking an AI Engineer to build and implement AI systems for content production at scale. You'll work at the intersection of engineering and content designing prompt pipelines, integrating generative models, and building the tooling that turns source material into finished creative output. The ideal candidate is technically strong but also has taste: someone who understands story and craft, and can tell the difference between output that's technically correct and output that's actually good.

Responsibilities

• Build and iterate on prompt pipelines and multi-agent workflow components
• Design and integrate agentic workflows orchestrate multi-step, tool-using agents that plan, call models, and hand off between stages in production
• Deploy and serve open-source models set up inference endpoints, manage GPU compute, and optimize for latency and cost
• Write evals compare outputs against references, quantify quality, and feed results back into the pipeline
• Work on data pipelines: structured extraction from messy source text, localization, similarity/dedup
• Debug and maintain pipeline stages in production

What you bring:  

• (1+/3+) years of engineering experience, or a strong portfolio of shipped projects
• Solid Python fundamentals clean, working, readable code
• Hands-on experience with LLM APIs and prompt engineering (personal projects count)
• Comfort with Git, REST APIs, and working in a Linux environment
• A feel for content and narrative you can judge whether generated output is actually good, not just valid
• Curiosity and clear communication you ask good questions and don't stay stuck silently

 Preferred

• Exposure to agent/orchestration frameworks (LangGraph, LangChain, CrewAI)
• Familiarity with vector databases, embeddings, or RAG (Qdrant, pgvector)
• Hands-on work with open-source generative media models Flux, LTX, Wan, or similar
• Experience deploying open-source models for inference (vLLM, ComfyUI, Replicate/Cog, Docker + GPU)
• Experience writing evals or LLM-as-judge scoring
• Node.js and Fastapi familiarity, or experience deploying on AWS

Strong AI Engineer / Machine Learning Engineer profiles.

2

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.

3

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

4

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

5

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.

6

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

7

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.

8

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

9

Mandatory (CTC) – The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.

10

Mandatory (Age) - Candidate's Age should be below 28 Years

AI Engineer

LLMs, Agents & AI Services

📍 Mumbai (On-site) | Full-time | 2-4 years

About the Role:

Unico Connect is an AI-first technology partner that builds custom mobile, web, and AI products for clients across multiple geographies.

AI is core to how we design, deliver, and scale software for our customers.

We are hiring an AI Engineer for a dedicated client engagement building a complex production AI platform, working on the AI capabilities and agentic features at the core of the product.

The mandatory requirement for this role is at least one AI feature personally shipped to production for real users, with operational ownership.

The role suits someone who thinks quickly on solutioning, can take an ambiguous problem to a working prototype in days, and has the discipline to carry it through to production with predictable economics.

You will work alongside the Senior AI Engineer and the wider pod, with ownership of parts of the AI surface area of the product.

Responsibilities:

Solutioning and POCs

Translate ambiguous customer problems into working POCs at speed.

Pick the right model, framework, and architecture, and demonstrate value early before scaling investment.

LLM Application Development

Build AI features and services using LLM APIs from OpenAI, Anthropic, Google, and self-hosted open-weight models (Llama, Qwen, Mistral).

Choose the right model per use case based on cost, latency, capability, and context-window trade-offs.

Agentic System Design

Design and implement agentic workflows using LangGraph, CrewAI, AutoGen, LlamaIndex Agents, or custom orchestration.

Cover tool use, planning, memory, and multi-step reasoning appropriate to the problem.

API and Service Development

Build production AI services and APIs using Python and FastAPI.

Handle streaming responses, async processing, structured outputs, retries, and graceful degradation when models or tools fail.

Retrieval and Tool Integration

Implement RAG pipelines with vector databases (Pinecone, Weaviate, Qdrant, pgvector, Chroma), embeddings, chunking strategies, hybrid search, and reranking.

Integrate external tools, internal APIs, and document sources through tool-calling and MCP-style patterns.

Cost Analysis and Unit Economics

Model the per-request and per-user cost of every AI feature before it ships.

Track token usage, prompt caching, batching, and model-routing strategies.

Drive measurable improvements in unit economics.

Production Hardening

Add observability and tracing (LangSmith, Langfuse, OpenTelemetry), guardrails, content safety checks, prompt injection defences, and fallback behaviour.

Prompt Engineering and Evaluation

Design, test, and iterate prompts with measured outcomes.

Build evaluation harnesses for accuracy, hallucination, latency, and cost.

Run benchmarks across models and prompt variants before locking in a design.

Requirements:

AI Feature Shipped to Production (Mandatory)

Must have personally built and shipped at least one AI feature that runs in production for real users, with operational ownership.

POCs, internal demos, and one-off scripts do not qualify.

2 to 4 Years of Professional Software or AI Engineering Experience

With at least one production AI feature owned end to end.

Strong Python Proficiency and API Development with FastAPI

Comfort with type hints, async, packaging, testing, streaming responses, and authentication.

Production-grade Python, not notebook-only code.

Hands-on Depth Across the LLM and Agent Stack

Working experience with at least two of OpenAI, Anthropic Claude, Google Gemini, or self-hosted open-weight models (vLLM, Ollama, Together, Replicate).

Working familiarity with at least one agent framework (LangGraph, CrewAI, AutoGen, LlamaIndex Agents) or hand-rolled equivalent.

Working knowledge of RAG, embeddings, and vector databases (Pinecone, Weaviate, Qdrant, pgvector, Chroma).

Solutioning Speed and POC Velocity

Demonstrated ability to move from a fuzzy problem to a working prototype in days.

Strong instinct for what to build first, what to defer, and what to throw away.

Cost Discipline for Production AI

Ability to calculate, monitor, and optimise the cost of LLM APIs, tokens, embeddings, vector store usage, and infrastructure.

Treats unit economics as a first-class concern.

AWS Familiarity

Working knowledge of EC2, S3, IAM, and at least one of Bedrock, SageMaker, or equivalent.

Comfortable in a Fast-Moving Environment

Self-directed, comfortable with ambiguity, takes ownership without being asked, and ships under shifting priorities.

Strong Written and Spoken English Communication

Able to explain trade-offs to non-AI engineers, designers, product managers, and clients in plain language.

Nice to Have

• fine-tuning or LoRA, QLoRA, PEFT exposure
• MCP server authoring
• eval framework experience (LangSmith, Promptfoo, Ragas, DeepEval)
• open-source AI contributions
• multi-modal models (vision, audio)

Job Description:

We are looking for a hands-on AI Engineer with experience in Generative AI and Agentic AI to build and deploy production-ready AI solutions.

Key Responsibilities:

• Develop and deploy GenAI and Agentic AI applications.
• Build RAG pipelines, LLM workflows, and AI agents.
• Develop solutions using Python, LangChain, LangGraph, LlamaIndex, or similar frameworks.
• Implement tool calling, context retrieval, and LLM orchestration.
• Integrate AI solutions with APIs and cloud platforms.
• Work with AWS/Azure/GCP, Docker, and CI/CD.

Required Skills:

• Strong Python programming skills.
• 3+ years of GenAI/Agentic AI experience.
• RAG and LLM orchestration.
• LangChain / LangGraph / LlamaIndex / AutoGen / CrewAI / Semantic Kernel.
• MCP and A2A knowledge.
• Cloud, APIs, Docker, and CI/CD experience.

Preferred Experience:

Hands-on experience building and deploying production-ready AI solutions.

Kody Technolab Limited is seeking an experienced AI/ML Engineer to design, develop, and deploy cutting-edge Artificial Intelligence and Machine Learning solutions. The ideal candidate will have

strong expertise in Machine Learning, Deep Learning, Generative AI, LLMs, MLOps, and cloud-based AI deployments.

Key Responsibilities

• Design, develop, and deploy Machine Learning and Deep Learning models for classification, regression, recommendation systems, NLP, Computer Vision, and Generative AI applications.

• Build and maintain end-to-end ML pipelines including data preprocessing, feature engineering, model training, validation, evaluation, and deployment.

• Develop AI solutions using PyTorch, TensorFlow, Scikit-learn, Hugging Face, and related frameworks.

• Work with Large Language Models (LLMs) and foundation models such as GPT, BERT, Llama, Claude, and Stable Diffusion.

• Collaborate with product, engineering, and business teams to translate requirements into scalable AI solutions.

• Optimize model performance, scalability, and reliability for production environments.

• Implement MLOps best practices using tools such as MLflow, Docker, Kubernetes, and Kubeflow.

• Stay updated with emerging trends and research in AI, ML, Deep Learning, and Generative AI.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Mathematics, or a related field.

• 7+ years of hands-on experience in AI/ML product development.

• Strong proficiency in Python and ML frameworks including Scikit-learn, TensorFlow, PyTorch, and Hugging Face.

• Experience with Generative AI, LLMs, GANs, VAEs, diffusion models, and prompt engineering.

• Strong understanding of the ML lifecycle including model training, tuning, deployment, monitoring, and optimization.

• Experience with MLOps tools such as MLflow, Docker, Kubeflow, and CI/CD pipelines.

• Experience with AWS, Azure, or GCP cloud platforms.

• Strong problem-solving and analytical skills.

Preferred Skills

• Fine-tuning and deployment of Large Language Models.

• Experience with RAG (Retrieval Augmented Generation) architectures.

• Contributions to open-source AI projects or research publications.

• Knowledge of model interpretability, data annotation, and feature engineering.

• C++ experience for high-performance AI applications.

Why Join Kody Technolab Limited?

Opportunity to work on innovative AI products, Generative AI solutions, robotics integrations,

and enterprise-scale applications while collaborating with a highly skilled technology team.

Visit the Website to know more about us.

Company Website - Kody Technolab | Deep Tech Company in Robotics & AI Solution

Kody Robots | Robotics Company in India for Autonomous Robots

Hiring for AI Engineer

Exp: 6 - 12 yrs

Edu : BE/B.Tech/MCA

Work Location : Pune / Mumbai

Skill Set:

Total experience ranging from 6–10 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

2+ years shipping LLM systems in production

Experience with cloud platforms (AWS/Azure/GCP)