Key Responsibilities

Build GenAI/agentic systems (chat copilots, workflow/graph agents, tool use, memory)
Implement chunking, hybrid search, vector stores, re-ranking, feedback loops, and continuous data quality/eval.
Select/integrate/finetune LLMs & multimodal models.
Apply prompt-engineering techniques to specific use cases and types
Experience working on solutions based on LLM, NLP, DL (Deep Learning), ML (Machine Learning), object detection / classification etc.
Should have good understanding of DevOps
Should have good understanding of LLM evaluation
Should have deployed min of 2 models in production (MLOps)
Should have understanding of guardrails, policy filters, PII redaction, runtime monitors and agent observability.
Unit testing of GenAI Solutions built and documentation of results.
Collaborate with clients/Stakeholders from multiple Geographies.
Stay informed about the latest advancements in Gen AI, machine learning, and AI technologies to optimize our technical stack.

Qualifications

Bachelor’s/Master’s Degree or equivalent.
5+ years in ML engineering, around 1+ years of hands-on with LLMs/GenAI and agentic frameworks.
Experience in shipping production AI systems on at least one hyperscaler (Azure/AWS/GCP).
Experience delivering end-to-end GenAI based solutions.

Required Skills

Strong Python experience to build multiple AI-ML/ GenAI Solutions.
Should have experience working on Agent orchestration with leading frameworks like LangGraph, LangChain, Semantic Kernel, CrewAI, AutoGen.
Strong experience working on SQL Query, Vector DB like Pinecone, Qdrant, Fiaas
Should have experience working on hybrid search and re-rankers
Should have experience on evaluation & observability LangSmith/ human-in-the-loop workflows.
Strong experience in using any one of the leading Hyperscaler services from Azure/AWS/GCP
Experience working on NLP, CV, Deep Learning Algorithms
Should have working experience with Open-Source Models
Experience on UI/UX will be added advantage.

Key Responsibilities

Build GenAI/agentic systems (chat copilots, workflow/graph agents, tool use, memory)
Implement chunking, hybrid search, vector stores, re-ranking, feedback loops, and continuous data quality/eval.
Select/integrate/finetune LLMs & multimodal models.
Apply prompt-engineering techniques to specific use cases and types
Experience working on solutions based on LLM, NLP, DL (Deep Learning), ML (Machine Learning), object detection / classification etc.
Should have good understanding of DevOps
Should have good understanding of LLM evaluation
Should have deployed min of 2 models in production (MLOps)
Should have understanding of guardrails, policy filters, PII redaction, runtime monitors and agent observability.
Unit testing of GenAI Solutions built and documentation of results.
Collaborate with clients/Stakeholders from multiple Geographies.
Stay informed about the latest advancements in Gen AI, machine learning, and AI technologies to optimize our technical stack.

Qualifications

Bachelor’s/Master’s Degree or equivalent.
5+ years in ML engineering, around 1+ years of hands-on with LLMs/GenAI and agentic frameworks.
Experience in shipping production AI systems on at least one hyperscaler (Azure/AWS/GCP).
Experience delivering end-to-end GenAI based solutions.

Required Skills

Strong Python experience to build multiple AI-ML/ GenAI Solutions.
Should have experience working on Agent orchestration with leading frameworks like LangGraph, LangChain, Semantic Kernel, CrewAI, AutoGen.
Strong experience working on SQL Query, Vector DB like Pinecone, Qdrant, Fiaas
Should have experience working on hybrid search and re-rankers
Should have experience on evaluation & observability LangSmith/ human-in-the-loop workflows.
Strong experience in using any one of the leading Hyperscaler services from Azure/AWS/GCP
Experience working on NLP, CV, Deep Learning Algorithms
Should have working experience with Open-Source Models
Experience on UI/UX will be added advantage.

Mid level GenAI Engineers

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