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Senior Data Science Lead

Pay
Pay not listed
Work setting
Hybrid — work setting source
Work setup in the employer’s posting: hybrid
From the employer’s posting
Employment
Not stated

What you’ll work on

Full posting
  • Design and build multi-agent AI systems using LangGraph

  • Implement knowledge graphs, vector databases and RAG for agent reasoning

  • Apply RLHF/RLAIF to fine-tune agents for better decisions

  • Lead agentic AI proof-of-concept projects and scale them to production

  • Lead and mentor AI Engineers and Data Scientists

From the employer’s posting
Design and develop multi-agent AI systems using LangGraph for workflow automation
Implement knowledge graphs, vector databases (Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG) techniques for enhanced agent reasoning
Apply reinforcement learning (RLHF/RLAIF) methodologies to fine-tune AI agents for improved decision-making
Lead Agentic AI proof-of-concept (PoC) projects that demonstrate tangible business impact and scale successful prototypes into production
Lead and mentor a team of AI Engineers and Data Scientists
Apply at Brillio-2

Posting record

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Open 6 monthsWe first saw this posting 205 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.

Status in our records
Live
First seen by us
Apr 15, 2026205 days ago
Recorded sightings
313
Last seen by us
Nov 6, 2026
Employer says posted
Jun 5, 2025

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Time since we first saw it

Day 205

Up longer than most. Similar jobs usually come down between day 6 and day 71.

Well past Brillio-2’s usual: its jobs come down by about day 25.

From 4,617 similar postings that came down, and 58 of Brillio-2’s. Coming down is not always a hire.

Brillio-2 is hiring

46 open jobs

Open jobs by the week we first saw them, last 12 weeks.

  • ML Engineer hiring is steady
  • Takes jobs down at a usual pace
Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

  • Python
  • R
  • SAS
  • Keras
  • PySpark
  • PyTorch
  • TensorFlow
Where the posting names them
Primary Skills - Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, Statistical analysis and computing, Probabilistic Graph Models, Great Expectation, Evidently AI, Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Tools(KubeFlow, BentoML), Classification (Decision Trees, SVM), ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), R/ R Studio Job requirements

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Demand for ML Engineer roles

New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.

Weekly new ML Engineer postings at the same employers, from the week of August 17 to the week of October 26. The fewest in a week was 624, the most 1,094, and the latest week had 870.
August 17Last week

Each bar is one week of new postings. The darkest bars are the last 5 weeks.

Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.

About the role

We file this job under ML Engineer, by the work the posting describes.

ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.

Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.

A data scientist often works out which model to build; an ML engineer makes it run reliably in production.

US government figures for computer and information research scientists

Median yearly pay, May 2025
$140,300
Expected change, 2025 to 2035
21.8% more jobsagainst 3.5% for all jobs

The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.

Job description

View original posting ↗

Senior Data Science Lead

Primary Skills

  • Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, Statistical analysis and computing, Probabilistic Graph Models, Great Expectation, Evidently AI, Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Tools(KubeFlow, BentoML), Classification (Decision Trees, SVM), ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), R/ R Studio

Job requirements

  • JD is below: The Agentic AI Lead is a pivotal role responsible for driving the research, development, and deployment of semi-autonomous AI agents to solve complex enterprise challenges. This role involves hands-on experience with LangGraph, leading initiatives to build multi-agent AI systems that operate with greater autonomy, adaptability, and decision-making capabilities. The ideal candidate will have deep expertise in LLM orchestration, knowledge graphs, reinforcement learning (RLHF/RLAIF), and real-world AI applications. As a leader in this space, they will be responsible for designing, scaling, and optimizing agentic AI workflows, ensuring alignment with business objectives while pushing the boundaries of next-gen AI automation. ________________________________________ Key Responsibilities 1. Architecting & Scaling Agentic AI Solutions • Design and develop multi-agent AI systems using LangGraph for workflow automation, complex decision-making, and autonomous problem-solving. • Build memory-augmented, context-aware AI agents capable of planning, reasoning, and executing tasks across multiple domains. • Define and implement scalable architectures for LLM-powered agents that seamlessly integrate with enterprise applications. 2. Hands-On Development & Optimization • Develop and optimize agent orchestration workflows using LangGraph, ensuring high performance, modularity, and scalability. • Implement knowledge graphs, vector databases (Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG) techniques for enhanced agent reasoning. • Apply reinforcement learning (RLHF/RLAIF) methodologies to fine-tune AI agents for improved decision-making. 3. Driving AI Innovation & Research • Lead cutting-edge AI research in Agentic AI, LangGraph, LLM Orchestration, and Self-improving AI Agents. • Stay ahead of advancements in multi-agent systems, AI planning, and goal-directed behavior, applying best practices to enterprise AI solutions. • Prototype and experiment with self-learning AI agents, enabling autonomous adaptation based on real-time feedback loops. 4. AI Strategy & Business Impact • Translate Agentic AI capabilities into enterprise solutions, driving automation, operational efficiency, and cost savings. • Lead Agentic AI proof-of-concept (PoC) projects that demonstrate tangible business impact and scale successful prototypes into production. 5. Mentorship & Capability Building • Lead and mentor a team of AI Engineers and Data Scientists, fostering deep technical expertise in LangGraph and multi-agent architectures. • Establish best practices for model evaluation, responsible AI, and real-world deployment of autonomous AI agents. ________________________________________

Employment type

Employee

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Location & working pattern

Chennai, Tamil Nadu, India

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