Senior ML Engineer

**NOTE: Only immediate joiners and experienced candidates needed**

Location

Budget

Notice Period

Experience

Responsibilities

Drive end-to-end AI application development—from idea conception and hypothesis formulation through rigorous testing to production deployment and monitoring, ensuring solutions solve real-world problems.

Embrace a boundaryless approach to technology, collaborating across backend systems, frontend interfaces (where applicable), automation scripts, and data pipelines to deliver holistic solutions.

Design, implement, and meticulously fine-tune LLM prompts, embeddings, RAG pipelines, and sophisticated AI agents (using frameworks like Langchain, CrewAI, Bedrock Agents, OpenAI SDK, Autogen etc.) tailored to specific domain use cases and business objectives.

Architect and build Generative AI solutions that are inherently scalable, secure, cost-efficient, and privacy-conscious, integrating these considerations from the initial design phase.

Implement robust testing methodologies and MLOps practices specifically for GenAI, focusing on preventing prompt drift, mitigating hallucinations, ensuring model regression testing, and maintaining high performance.

Maintain exceptionally high standards in code hygiene, implement robust CI/CD practices for AI models and applications, and produce clear, comprehensive documentation.

Collaborate closely with data analysts and engineers to guarantee data quality, readiness, and the efficiency of data pipelines feeding into GenAI models and applications.

Formulate and validate hypotheses by engaging directly with stakeholders, understanding their needs deeply, and translating them into actionable technical requirements.

Champion rapid prototyping and lean experimentation methodologies: build fast, fail fast, learn faster, and iterate quickly to test ideas before committing significant resources.

Engage proactively and cross-functionally with Product Management, Design, Business Analysts, Security, and Legal teams to navigate complex requirements and deliver impactful solutions.

Actively contribute to a vibrant culture of learning and innovation by creating tech blogs, developing internal Proofs-of-Concept (POCs), delivering demos, and conducting workshops on GenAI topics.

Stay relentlessly current with the rapidly evolving Generative AI landscape, including new trends, models, tools (like AWS Bedrock, Azure OpenAI, Vertex AI), and research advancements, critically evaluating opportunities for integration and improvement.

Provide technical leadership and mentorship to junior engineers, fostering a culture of technical excellence and continuous learning within the GenAI space.

Requirements

6+ years of hands-on Machine Learning, CI/CD, and MLOps experience (must have productionized models).

3+ years of implementation experience as a customer-facing consultant

Experience with:

Agent Bricks

MosaicAI

MLflow

Apache Spark Programming

Scalable Machine Learning

Machine Learning in Production

Databricks ML Ops stack