Senior AI/ML Engineer (GEN AI) -Pune/Hyd

Tekskills Inc.; Hyderabad; On-site; INR 2,500,000 - 4,500,000; Full time

Job summary

Tekskills Inc. in India is seeking a highly experienced Senior AI/ML Engineer with deep NLP, Generative AI, and cloud-native ML systems to drive production-ready models and scalable AI-first applications.

The role emphasizes hands-on design of intent detection, NLG pipelines, LangChain/LangGraph based workflows, and AWS Bedrock integration, collaborating with product and backend teams to deliver robust AI solutions.

Qualifications

• Total 10+ in IT with 4 to 5+ years of experience in machine learning, focused on NLP and Generative AI.
• Proficient in LangChain, LangGraph, vector databases, and orchestration of LLM workflows.

Responsibilities

• Design, develop, and deploy intent classification and detection models using LLMs and NLP methods.
• Build and optimize NLG pipelines for chatbot responses, summarization, or knowledge grounding.
• Architect LangChain and LangGraph based applications for LLM-driven workflows (autonomous agents, RAG).
• Develop scalable ML pipelines using AWS (Sagemaker, Lambda, Bedrock, Step Functions, DynamoDB, Athena).
• Integrate and fine-tune foundation models via AWS Bedrock (Titan, Claude, Llama).
• Collaborate with product managers, ML researchers, and backend engineers to translate requirements into AI solutions.
• Lead experimentation, conduct A/B testing, ensure continuous evaluation of deployed models.
• Mentor junior ML engineers and contribute to MLOps best practices.

Skills

NLP

Generative AI

MLOps

Python

CI/CD for ML

Education

Bachelor's or Master's in Computer Science or Data Science

Tools

LangChain

LangGraph

FAISS

Pinecone

Weaviate

AWS Bedrock

SageMaker

Lambda

DynamoDB

Step Functions

Kinesis

PyTorch

HuggingFace Transformers

scikit-learn

Job description

We are seeking a highly experienced Senior AI/ML Engineer with deep expertise in Natural Language Processing (NLP), Generative AI, and cloud-native ML systems. This role is ideal for someone who has built production-ready intent detection models, NLG systems, and has strong experience with AWS Bedrock, LangChain, and LangGraph. Youll play a key role in architecting and scaling AI-first applications that leverage the latest in LLM, orchestration, and AWS-native services.

Key Responsibilities:

• Design, develop, and deploy intent classification and intent detection models using LLMs and traditional NLP methods.
• Build and optimize Natural Language Generation (NLG) pipelines for chatbot responses, summarization, content creation, or knowledge grounding.
• Architect and implement LangChain and LangGraph based applications for LLM-driven workflows (e.g., autonomous agents, RAG systems).
• Develop scalable machine learning pipelines using the AWS tech stack (e.g., Sagemaker, Lambda, Bedrock, Step Functions, DynamoDB, Athena).
• Integrate and fine-tune foundation models via AWS Bedrock, including Amazon Titan, Anthropic Claude, or Meta Llama.
• Collaborate closely with product managers, ML researchers, and backend engineers to translate business requirements into robust AI solutions.
• Lead experimentation efforts, conduct A/B testing, and ensure continuous evaluation of deployed ML models.
• Mentor junior ML engineers and contribute to best practices in MLOps, model governance, and responsible AI.

Required Qualifications:

• Total 10+ in IT with 4 to 5 + years of experience in machine learning, with a focus on NLP and Generative AI.
• Strong experience building and deploying intent detection, text classification, sequence tagging, and entity recognition models.
• Proficient in LangChain, LangGraph, vector databases (e.g., FAISS, Pinecone), and orchestration of LLM workflows.
• Deep knowledge of AWS Bedrock, Amazon SageMaker, Lambda, DynamoDB, Step Functions, etc.
• Experience working with open-source LLMs (LLaMA, Mistral, Falcon) or commercial APIs (Claude, GPT-4, etc.).
• Proficient in Python, with a solid grasp of ML frameworks such as PyTorch, HuggingFace Transformers, scikit-learn.
• Strong understanding of MLOps practices including model versioning, CI/CD for ML, monitoring, and auto-scaling.
• Bachelor’s or Master’s in Computer Science, Data Science, or a related field.

Nice to Have:

• Experience integrating RAG (Retrieval-Augmented Generation) systems at scale.
• Familiarity with vector search using Amazon OpenSearch, Pinecone, or Weaviate.
• Experience with streaming data processing (e.g., AWS Kinesis, Kafka).
• Contributions to open-source AI/ML or NLP projects.