Data Scientist/ AI Agent

Preferred Skills (Bonus Points):

• Familiarity with

  multi-agent orchestration frameworks

  .
• Experience in

  human-in-the-loop AI

  ,

  knowledge extraction

  , or

  clinical/pharma R&D

  .
• Knowledge of

  Responsible AI principles

  ,

  red teaming

  , or

  AI risk assessment

  .
• Strong backend development experience with emphasis on

  code quality

  ,

  logging

  , and

  automated testing

  .
• Exposure to

  semantic search

  ,

  vector databases

  , or

  embedding optimization techniques

  .

Why Join Us?

• Work at the cutting edge of

  GenAI, multi-agent systems, and enterprise automation

  .
• Build impactful AI solutions in domains such as

  healthcare

  ,

  life sciences

  ,

  R&D

  , and

  finance

  .
• Collaborate with world-class engineers, scientists, and innovators in a high-performance team.
  
• Opportunity to define and shape the

  next-gen AI platforms

  and infrastructure.

About the Role:

AI/ML Engineer

annotation automation, knowledge graph generation, drug discovery, clinical R&D, and enterprise data orchestration

Key Responsibilities:

• Design & Develop GenAI Agents:

  Build intelligent agents using frameworks like

  LangChain

  ,

  AutoGen

  ,

  LangGraph

  , or

  Semantic Kernel

  for tasks such as summarization, labeling, document classification, and data annotation.

• Orchestrate Multi-Agent Systems:

  Implement memory/state management, decision-making strategies, and inter-agent communication using LLMs and reinforcement learning.

• LLMOps & Pipeline Development:

  Develop

  end-to-end LLMOps pipelines

  for fine-tuning, deployment, monitoring, and evaluation of LLMs using

  MLflow

  ,

  Azure

  ,

  GCP

  , or

  AWS

  .

• Responsible AI & Governance:

  Enforce compliance with governance frameworks (GDPR, AI Act) and embed

  explainability, fairness, and transparency

  into AI systems.

• Security & Infrastructure:

  Integrate secure deployment practices, access controls, model sandboxing, and cloud-native CI/CD systems for scalable GenAI products.

• RAG & Vector Search:

  Build

  Retrieval-Augmented Generation (RAG)

  pipelines using

  FAISS

  ,

  Pinecone

  , or similar tools, and optimize LLM context windows and embeddings.

• Model Lifecycle & Observability:

  Automate training, fine-tuning (PEFT, RLHF), rollback, and real-time monitoring for GenAI agents and models.

• Collaboration & Impact:

  Work closely with

  MLOps, DevSecOps, Data Scientists

  , and

  product teams

  to drive real-world GenAI applications in healthcare, pharma, finance, and enterprise data systems.

Required Qualifications:

• Bachelor's or Master's degree in Computer Science, AI/ML, Engineering, or related fields.
  
• 36 years of experience in AI/ML Engineering with at least

  12 years in GenAI/LLM-based systems

  .
• Strong hands-on experience with

  Python

  ,

  PyTorch/TensorFlow

  , and LLM frameworks like

  LangChain

  ,

  LlamaIndex

  ,

  Hugging Face

  , or

  AutoGen

  .
• Proficiency in cloud platforms:

  Azure

  ,

  GCP

  , or

  AWS

  and containerization tools like

  Docker

  .
• Sound understanding of

  prompt engineering

  ,

  NLP

  ,

  Reinforcement Learning

  , and

  model evaluation

  .
• Experience building or integrating

  agentic frameworks

  ,

  LLM pipelines

  , and

  AI observability systems

  .

Apply Now if You Have:

A passion for building intelligent, autonomous AI systems
Proven track record of deploying LLM-based applications at scale A drive to create real-world impact with GenAI