Lead Software Engineer - ML

As a Lead Software Engineer for the Machine Learning team, you will act as the critical link between cutting-edge AI research and product-ready implementation. With a heavy focus on Generative AI and Large Language Models (LLMs), you will translate sophisticated concepts into scalable, highly reliable systems. Your work will directly accelerate our AI innovation, transforming Agentic frameworks and RAG architectures into production-grade features that serve millions of requests.
Responsibilities:
Advanced AI Implementation: Design, build, and deploy robust AI applications leveraging Retrieval-Augmented Generation (RAG) and various Agentic frameworks to solve complex business challenges.
Full-Stack ML Delivery: Write high-performance, production-grade code to build extensible ML APIs and scalable web services using Python, JavaScript, and TypeScript.
Hands-On System Design: Architect complex technical solutions from scratch. Lead the hands-on design of distributed systems, ensuring technical alignment across product and engineering teams.
Prompt Engineering and Optimization: Apply advanced prompt engineering techniques to maximize LLM performance, accuracy, and efficiency within our product ecosystem.
AI Tooling and Ecosystems: Continuously evaluate and integrate modern AI tooling (vector databases, orchestration layers, and LLMOps platforms) to keep our tech stack at the cutting edge.
End-to-End Pipeline Architecture: Build and manage comprehensive ML pipelines, including data preprocessing, automated deployment, cross-validation, and active feedback loops.
Prototyping and POC Execution: Lead Proof of Concept (POC) initiatives to identify and validate optimal frameworks, tech stacks, and AI models for new product features.
Operational Intelligence: Devise and build monitoring capabilities for system health, latency, token usage, and model performance metrics (e. g., accuracy, hallucination rates) to ensure long-term stability.
Requirements:
Experience: 6 to 9 years of highly relevant experience in software engineering and machine learning development.
Production Excellence: Proven track record of architecting, building, and productionizing Machine Learning or Generative AI solutions at an enterprise scale.
Education: Degree in Computer Science, Artificial Intelligence, Mathematics, or a related quantitative field.
Core Skills and Expertise:
Programming Languages: Expert-level coding capabilities in Python, JavaScript, and TypeScript.
Generative AI Mastery: Deep practical knowledge of Prompt Engineering, Retrieval-Augmented Generation (RAG) architectures, and diverse Agentic frameworks (e. g., LangChain, LlamaIndex, AutoGen).
System Design: Strong, hands-on experience in complex, distributed System Design and foundational engineering (Data Structures and Algorithms).
AI Tooling and Infrastructure: Familiarity with the modern AI ecosystem, including vector databases (e. g., Pinecone, Weaviate), LLM APIs, and cloud infrastructure for large-scale data processing.
MLOps / LLMOps: Practical experience with MLOps practices, ensuring seamless, automated, and secure model transitions from development into production environments.

Experience
5-9 yrs