Senior Machine Learning Engineer

Key Responsibilities

Design, implement, and optimize LLM pipelines using frameworks such as LangChain, LangGraph, and OpenAI / Anthropic APIs.

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 data scientists and 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.

Qualifications

To be successful in this role you have :

5+ years of hands on experience in ML / AI engineering, with a strong focus on building and deploying AI / GenAI applications.

Proficiency in Python (ML libraries, GenAI / LLM frameworks) and Java (enterprise application development, OOP).

Practical experience developing with LLM frameworks such as LangChain, LangGraph, and vendor SDKs / APIs (OpenAI, Anthropic, etc.).

Experience in using Al productivity tools such as Cursor, Windsurf, etc. is a plus or nice to have

Experience with methods of training and fine-tuning large language models, such as distillation, supervised fine-tuning, and policy optimization is a plus or nice to have.

Strong skills in prompt engineering and designing agentic reasoning pipelines.

Solid understanding of ML evaluation techniques and experience in implementing model monitoring & metrics.

Proven ability to debug and optimize inference pipelines for performance and cost efficiency.

Strong problem solving skills with the ability to work in fast paced, agile development environments.

Bonus : Contributions to open-source projects, blogs, or technical papers in LLM / GenA