Senior Machine Learning Engineer

About the team:

Agentic AI team

research initiatives

What you get to do in this role:

• Design, implement, and optimize

  LLM pipelines

  using frameworks such as

  LangChain

  ,

  LangGraph

  , and vendor APIs (OpenAI, Anthropic, etc.).
• Write clean, efficient, and scalable code in

  Python

  and

  Java

  , applying best practices in ML engineering and enterprise application development.
• Develop and refine

  prompt engineering strategies

  and

  agent reasoning workflows

  to improve accuracy and reliability.
• Build and maintain

  evaluation frameworks

  for measuring model performance, including custom metrics for agent instructions and reasoning.
• Debug, profile, and optimize

  inference pipelines

  for latency and cost efficiency at scale.
• Collaborate closely with product teams to translate business needs into technical implementations.
• Contribute to

  end-to-end development

  from prototyping to production deployment in enterprise environments.
• Stay current with the latest advancements in

  LLMs, Agentic AI

  , and orchestration frameworks, applying new techniques where beneficial.

To be successful in this role you have:

• Experience in leveraging and critically evaluating AI integration

  into work processes, decision-making, and problem-solving. This includes using AI-powered tools, automating workflows, analyzing AI-driven insights, and exploring AI s potential impact on business functions or the industry.

• 4 to 6+ years of hands-on experience

  designing and building AI/ML pipelines and solutions, with a strong emphasis on

  scalable, production-grade applications

  .
• Proficiency in

  Python

  (ML libraries and GenAI/LLM frameworks) and

  Java

  for enterprise application development and object-oriented design.
• Practical experience with

  LLM frameworks

  such as

  LangChain

  and

  LangGraph

  , as well as leading vendor SDKs and APIs (

  OpenAI

  ,

  Anthropic

  , etc.).
• Strong expertise in

  prompt engineering

  and designing

  agentic reasoning workflows and pipelines

  .
• Solid understanding of

  ML evaluation techniques

  , with hands-on experience implementing

  model monitoring and metrics

  .
• Proven ability to

  debug and optimize inference pipelines

  for both performance and cost efficiency.
• Strong

  problem-solving skills

  with the ability to thrive in

  fast-paced, agile development environments

  .

• Nice to have:

  Contributions to

  open-source projects

  , technical blogs, or papers related to

  LLMs and GenAI

  .