AI ML Lead

Job Title: Senior AI/ML Engineer Custom LLM & RAG Implementation

Location:

About the Role

Senior AI/ML Engineer

This role is strategic and technical, requiring a blend of research, solution engineering, MLOps maturity, and domain adaptability.

Key Responsibilities

• LLM Development & Deployment

• Design, build, and deploy

  customized LLM pipelines

  tailored to enterprise use cases.
• Implement end-to-end

  LLMOps workflows

  including model packaging, CI/CD, and monitoring.

• RAG & Fine-Tuning

• Implement

  RAG pipelines

  using vector databases (e.g., FAISS, Pinecone, Weaviate) and document ingestion frameworks (e.g., LangChain, Haystack).
• Fine-tune open-source LLMs (e.g., LLaMA, Falcon, Mistral, MPT) on proprietary datasets using frameworks like Hugging Face Transformers and PEFT/LoRA.

• Solution Engineering

• Translate business problems into ML/LLM solutions with

  clear problem framing and data strategy

  .
• Collaborate with product, data engineering, and domain teams to prototype and deliver scalable solutions.

• Cross-Functional AI Applications

• Apply ML/LLM solutions across

  multiple verticals

  such as legal document analysis, customer support automation, compliance, supply chain optimization, or medical NLP.
• Build

  domain-agnostic prompt engineering strategies

  and apply

  zero-shot/few-shot learning

  where appropriate.

• Leadership & Mentorship

• Mentor junior engineers and contribute to

  AI/ML best practices

  .
• Act as a thought partner in innovation and experimentation within the team and with external stakeholders.

Required Skills & Qualifications

• Bachelors or Master’s in Computer Science, AI/ML, Data Science, or related field.
• 5+ years of

  hands-on experience in ML/NLP

  , with a recent focus on LLMs and foundation models.
• Deep knowledge of

  Hugging Face ecosystem

  , PyTorch/TensorFlow, LangChain, OpenAI APIs, and popular model libraries.
• Experience deploying ML models to production using

  FastAPI, Docker, Kubernetes

  , or cloud-native tools (AWS/GCP/Azure).
• Familiarity with

  vector databases

  , embeddings, and search frameworks.
• Strong understanding of

  model evaluation metrics

  , bias/fairness in ML, and responsible AI practices.
• Ability to work cross-functionally with business, legal, and engineering teams.

Preferred Qualifications

• Experience with

  RLHF (Reinforcement Learning from Human Feedback)

  .
• Published work in open-source communities or AI research conferences.
• Familiarity with

  multi-modal AI

  ,

  autoML

  , or

  agentic workflows

  is a plus.
• Prior work in

  regulated domains

  (e.g., finance, healthcare, legal).

Why Join Us

• Work on

  cutting-edge AI/LLM use cases

  that span industries and functions.
• Lead

  mission-critical AI initiatives

  from ideation to deployment.
• Be part of a

  collaborative, innovation-driven team

  shaping the next generation of enterprise AI solutions.