LLM/AI Model Engineer – Training, Evaluation & Benchmarking

LLM/AI Model Engineer – Training, Evaluation & Benchmarking at QubeLabs Systems Private Limited · Delhi · 3 - 5 years · ₹10L - ₹20L / yr · Posted 9 Oct 2026

LLM/AI Model Engineer – Training, Evaluation & Benchmarking

at QubeLabs Systems Private Limited

Posted by Nitish Kumar

3 - 5 yrs

₹10L - ₹20L / yr

Delhi

Skills

LLM Evaluation Frameworks

Fine-tuning LLMs

Open-source LLMs

Python

PyTorch

Benchmarking

Deep Learning

Hugging Face Transformers

Huggingface

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.

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About QubeLabs Systems Private Limited

Founded

Type

Size

Stage

About

QubeLabs Workmate is a purpose-built Sovereign AI System for the Financial Services Industry — unifying workforce operations, intelligence, and enterprise execution at scale.

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What You Will Own

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• Define and track model-quality SLOs in production: transcription drift, diarization error over time, retrieval relevance, latency, throughput, and cost per user.

• Build dashboards and alerting so model degradation is caught before users feel it.

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For more information about us please visit our:

Our Company and Culture: https://bit.ly/3Iqm5SB

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Our LinkedIn Page: www.linkedin.com/company/leegality/

Leegality's Privacy Notice: https://www.leegality.com/employee-privacy-notice

This is a remote position.

About Leegality:

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Company: Timble Technologies Pvt. Ltd

Location: Gurugram (Hybrid)

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About Us

Timble Glance is a high-growth AI RegTech and B2B SaaS company catering to top-tier BFSI and enterprise clients. We build cutting-edge systems powering 30+ high-scale APIs for digital identity verification, fraud detection, document intelligence, and compliance automation.

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We are looking for a hands-on Senior AI/ML Engineer to design, develop, and productionize high-throughput AI/ML and Generative AI systems. You will own the full lifecycle—from problem formulation and data pipelines to deep learning architectures, RAG systems, LLMOps, and model governance—delivering sub-second latency and high reliability across our enterprise products.

Key Responsibilities

· Model Architecture & Deployment: Design, train, and deploy production-scale ML/Deep Learning and GenAI systems (computer vision, document intelligence, OCR, NLP, fraud risk classification, and LLM applications).

· GenAI & LLM Solutions: Develop robust LLM workflows including prompt engineering, fine-tuning, RAG pipelines, semantic search, vector indexing (Pinecone/Milvus/Chroma), and safety guardrails.

· Pipelines & Engineering: Build performant feature extraction and data pipelines; write modular, vectorized, production-grade Python (NumPy, Pandas) and advanced SQL.

· MLOps & Monitoring: Establish end-to-end MLOps/LLMOps standards—model registries, CI/CD, experiment tracking, drift detection, A/B testing, latency optimization, and cost governance.

· Responsible AI & Security: Ensure model decisions comply with enterprise data security, privacy standards, and auditability required by the BFSI sector.

· Collaboration & Ownership: Translate complex business requirements into technical roadmaps, conduct rigorous code reviews, and mentor junior engineers.

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· Education: B.Tech / M.Tech in Computer Science, AI/ML, Mathematics, or a related field—Tier-1 institutes (IIT, IIIT, NIT) strongly preferred.

· Experience: 2+ years of hands-on experience developing, deploying, and maintaining ML/Deep Learning or GenAI models in production environments.

· GenAI & NLP Stack: Hands-on experience with LLMs, embeddings, RAG architectures, and frameworks such as LangChain, LlamaIndex, or Hugging Face.

· Deep Learning Frameworks: Strong proficiency in PyTorch or TensorFlow, with deep knowledge of transformer architectures and modern NLP/CV models.

· Software & Data Engineering: Expert-level Python skills (pytest, Git, OOP, asynchronous programming), solid SQL proficiency, and familiarity with data workflows.

· Deployment & Cloud: Practical exposure to cloud platforms (AWS/GCP), containerization (Docker), API frameworks (FastAPI/Flask), and basic orchestration (Kubernetes).

Preferred Qualifications

· Prior domain experience in Fintech, RegTech, Identity Verification (KYC/AML), Fraud Intelligence, or B2B SaaS.

· Experience optimizing models for low latency and inference cost (e.g., ONNX, TensorRT, model quantization).

· Familiarity with workflow orchestrators such as Airflow, Prefect, or Kubeflow.

Job Title: Senior AI/ML Engineer

Company: Timble Technologies Pvt. Ltd

Location: Gurugram (Hybrid)

Experience: 2 TO 5 Years

About Us

Role Overview

Key Responsibilities

· GenAI & LLM Solutions: Develop robust LLM workflows including prompt engineering, fine-tuning, RAG pipelines, semantic search, vector indexing (Pinecone/Milvus/Chroma), and safety guardrails.

· Pipelines & Engineering: Build performant feature extraction and data pipelines; write modular, vectorized, production-grade Python (NumPy, Pandas) and advanced SQL.

· MLOps & Monitoring: Establish end-to-end MLOps/LLMOps standards—model registries, CI/CD, experiment tracking, drift detection, A/B testing, latency optimization, and cost governance.

· Responsible AI & Security: Ensure model decisions comply with enterprise data security, privacy standards, and auditability required by the BFSI sector.

· Collaboration & Ownership: Translate complex business requirements into technical roadmaps, conduct rigorous code reviews, and mentor junior engineers.

Required Qualifications & Skills

· Education: B.Tech / M.Tech in Computer Science, AI/ML, Mathematics, or a related field—Tier-1 institutes (IIT, IIIT, NIT) strongly preferred.

· Experience: 2+ years of hands-on experience developing, deploying, and maintaining ML/Deep Learning or GenAI models in production environments.

· GenAI & NLP Stack: Hands-on experience with LLMs, embeddings, RAG architectures, and frameworks such as LangChain, LlamaIndex, or Hugging Face.

· Deep Learning Frameworks: Strong proficiency in PyTorch or TensorFlow, with deep knowledge of transformer architectures and modern NLP/CV models.

· Software & Data Engineering: Expert-level Python skills (pytest, Git, OOP, asynchronous programming), solid SQL proficiency, and familiarity with data workflows.

· Deployment & Cloud: Practical exposure to cloud platforms (AWS/GCP), containerization (Docker), API frameworks (FastAPI/Flask), and basic orchestration (Kubernetes).

Preferred Qualifications

· Prior domain experience in Fintech, RegTech, Identity Verification (KYC/AML), Fraud Intelligence, or B2B SaaS.

· Experience optimizing models for low latency and inference cost (e.g., ONNX, TensorRT, model quantization).

· Familiarity with workflow orchestrators such as Airflow, Prefect, or Kubeflow.

Associate Lead - AI/ML Engineer ( Python, AL/ML) - tier 1 college onl

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

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

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

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

Machine Learning Engineer

at Egnyte

4 recruiters

Posted by Bhavana Kapalganti

Remote only

5 - 10 yrs

Best in industry

LoRA / QLoRA

Python

SLM

Large Language Models (LLM)

PyTorch

EGNYTE YOUR CAREER. SPARK YOUR PASSION.

Egnyte is a place where we spark opportunities for amazing people. We believe that every role has meaning, and every Egnyter should be respected. With 23,000 customers worldwide and growing, you can make an impact by protecting their valuable data. When joining Egnyte, you’re not just landing a new career; you become part of a team of Egnyters who are doers, thinkers, and collaborators who embrace and live by our values:

Invested Relationships

Fiscal Prudence

Candid Conversations

ABOUT EGNYTE

Egnyte is the secure multi-cloud platform for content security and governance that enables organizations to better protect and collaborate on their most valuable content. Established in 2008, Egnyte has democratized cloud content security for more than 23,000 organizations, helping customers improve data security, maintain compliance, prevent and detect ransomware threats, and boost employee productivity on any app, any cloud, anywhere.

WHAT YOU’LL DO:

Fine-tune and train SLMs using Hugging Face, TRL, and adapter methods (LoRA, QLoRA, PEFT)
Optimize models for inference via quantization, pruning, and knowledge distillation
Deploy models to edge devices, mobile, and local servers with strict latency targets
Build end-to-end MLOps pipelines from data ingestion to deployment
Monitor model accuracy, latency, and hardware utilization in production
Evaluate model quality using benchmarking frameworks and custom evaluation suites

YOUR QUALIFICATIONS:

SLM Development & Fine-tuning: Train and fine-tune SLMs using Hugging Face and Knowledge on Adaptors.
Model Optimization: Apply quantization, pruning, knowledge distillation, and optimization for lightweight, efficient models.
Edge Deployment: Deploy models to edge devices, mobile, and local servers, etc.
Pipeline Engineering: Build end-to-end MLOps pipelines — from data ingestion to deployment.
Performance Monitoring: Track model accuracy, latency, and CPU/GPU usage in production.

Good to have

Deployment experience on edge or mobile environments
Knowledge of ONNX export and cross-platform inference
MLOps tooling — experiment tracking, model registries, CI/CD for ML

EQUAL EMPLOYMENT OPPORTUNITY

At Egnyte, we celebrate our unique differences and thrive on our diversity for our employees, our products, our customers, our investors, and our communities. Our global Egnyte Employee Communities (EECs) support representation and inclusion across our diverse workplace. Egnyters are encouraged to bring their whole selves to work and to appreciate the many differences that collectively make Egnyte a higher-performing company and a great place to be.

Egnyte will not allow any form of retaliation against employees who raise issues of equal employment opportunity. To ensure the workplace is free of artificial barriers, violation of this policy including any improper retaliatory conduct will lead to discipline, up to and including discharge. All employees must cooperate with all investigations conducted pursuant to this policy.

EGNYTE YOUR CAREER. SPARK YOUR PASSION.

Invested Relationships

Fiscal Prudence

Candid Conversations

ABOUT EGNYTE

WHAT YOU’LL DO:

Fine-tune and train SLMs using Hugging Face, TRL, and adapter methods (LoRA, QLoRA, PEFT)
Optimize models for inference via quantization, pruning, and knowledge distillation
Deploy models to edge devices, mobile, and local servers with strict latency targets
Build end-to-end MLOps pipelines from data ingestion to deployment
Monitor model accuracy, latency, and hardware utilization in production
Evaluate model quality using benchmarking frameworks and custom evaluation suites

YOUR QUALIFICATIONS:

SLM Development & Fine-tuning: Train and fine-tune SLMs using Hugging Face and Knowledge on Adaptors.
Model Optimization: Apply quantization, pruning, knowledge distillation, and optimization for lightweight, efficient models.
Edge Deployment: Deploy models to edge devices, mobile, and local servers, etc.
Pipeline Engineering: Build end-to-end MLOps pipelines — from data ingestion to deployment.
Performance Monitoring: Track model accuracy, latency, and CPU/GPU usage in production.

Good to have

Deployment experience on edge or mobile environments
Knowledge of ONNX export and cross-platform inference
MLOps tooling — experiment tracking, model registries, CI/CD for ML

EQUAL EMPLOYMENT OPPORTUNITY

LLM Internship (Remote)

at Springer Capital

Posted by Andrew Rose

Remote only

0 - 0 yrs

₹3000 - ₹5000 / mo

Python

About the Role

We are looking for enthusiastic LLM Interns to join our team remotely for a 3-month internship. This role is ideal for students or graduates interested in AI, Natural Language Processing (NLP), and Large Language Models (LLMs). You will gain hands-on experience working with cutting-edge AI tools, prompt engineering, and model fine-tuning. While this is an unpaid internship, interns who successfully complete the program will receive a Completion Certificate and a Letter of Recommendation.

Responsibilities

Research and experiment with LLMs, NLP techniques, and AI frameworks.
Design, test, and optimize prompts and workflows for different use cases.
Assist in fine-tuning or integrating LLMs for internal projects.
Evaluate model outputs and improve accuracy, efficiency, and reliability.
Collaborate with developers, data scientists, and product managers to implement AI-driven features.
Document experiments, results, and best practices.

Requirements

Strong interest in Artificial Intelligence, NLP, and Machine Learning.
Familiarity with Python and ML libraries (e.g., TensorFlow, PyTorch, Hugging Face Transformers).
Basic understanding of LLM concepts such as embeddings, fine-tuning, and inference.
Knowledge of APIs (OpenAI, Anthropic, Hugging Face, etc.) is a plus.
Good analytical and problem-solving skills.
Ability to work independently in a remote environment.

What You’ll Gain

Practical exposure to state-of-the-art AI tools and LLMs.
Mentorship from AI and software professionals.
Completion Certificate upon successful completion.
Letter of Recommendation based on performance.
Experience to showcase in research projects, academic work, or future AI roles.

Internship Details

Duration: 3 months
Location: Remote (Work from Home)
Stipend: Unpaid
Perks: Completion Certificate + Letter of Recommendation

About the Role

Responsibilities

Research and experiment with LLMs, NLP techniques, and AI frameworks.
Design, test, and optimize prompts and workflows for different use cases.
Assist in fine-tuning or integrating LLMs for internal projects.
Evaluate model outputs and improve accuracy, efficiency, and reliability.
Collaborate with developers, data scientists, and product managers to implement AI-driven features.
Document experiments, results, and best practices.

Requirements

Strong interest in Artificial Intelligence, NLP, and Machine Learning.
Familiarity with Python and ML libraries (e.g., TensorFlow, PyTorch, Hugging Face Transformers).
Basic understanding of LLM concepts such as embeddings, fine-tuning, and inference.
Knowledge of APIs (OpenAI, Anthropic, Hugging Face, etc.) is a plus.
Good analytical and problem-solving skills.
Ability to work independently in a remote environment.

What You’ll Gain

Practical exposure to state-of-the-art AI tools and LLMs.
Mentorship from AI and software professionals.
Completion Certificate upon successful completion.
Letter of Recommendation based on performance.
Experience to showcase in research projects, academic work, or future AI roles.

Internship Details

Duration: 3 months
Location: Remote (Work from Home)
Stipend: Unpaid
Perks: Completion Certificate + Letter of Recommendation

AI/ML Lead

at Kody Technolab

Posted by Hinal Shah

Ahmedabad

7 - 9 yrs

₹13L - ₹30L / yr

Large Language Models (LLM)

Generative AI

GPT

BERT

LAMA

+4 more

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