Lead Software Engineer - Machine Learning

Job Description

As a Machine Learning Engineer in the Agentic AI team, you will be at the forefront of the next paradigm shift in artificial intelligence. You will transition from static models to building autonomous, goal-driven intelligent agents capable of dynamic reasoning, tool execution, and complex workflow orchestration. Your role is to bridge the gap between bleeding-edge generative AI research and enterprise-scale, production-ready agentic systems.

Impact You Will Create

• Productionize Autonomous Systems: Serve as the critical link translating advanced research in LLMs, multi-agent frameworks, and cognitive architectures into highly reliable, product-ready, enterprise-scale implementations.

• Scale Multi-Agent Workflows: Build and deploy robust ML infrastructures, runtime environments, and data pipelines engineered to orchestrate and execute millions of autonomous agent decisions with low latency, high efficiency, and safety guardrails.

• Architect Next-Gen AI Frameworks: Drive technical alignment by designing scalable agentic frameworks from the ground up, establishing best practices for prompt engineering frameworks, memory systems, and tool-use integration across the organization.

Roles & Responsibilities

• Agentic Framework Implementation: Collaborate with AI Researchers and Data Scientists to translate complex reasoning loops (e.g., ReAct, Reflection, Tree of Thoughts) and experimental algorithms into clean, production-grade code.

• End-to-End Agent Architecture: Design, build, and manage comprehensive agent pipelines, including state management, long/short-term vector memory systems, automated tool-calling integrations, and dynamic feedback loops.

• High-Performance Service Delivery: Develop and deploy extensible, scalable API microservices optimized to minimize latency during heavy token generation, parallel agent execution, and high-concurrency traffic loads.

• Operational Telemetry & Evaluation: Design and implement robust tracking frameworks to monitor agent drift, hallucination rates, cost/token efficiency, and multi-step execution accuracy to ensure systemic reliability.

• Strategic Ecosystem Collaboration: Architect foundational AI primitives from scratch and liaise with cross-product architects to ensure seamless integration of agentic capabilities into existing product ecosystems.

• Prototyping & LLM Benchmarking: Lead Proof of Concept (POC) initiatives utilizing diverse tech stacks, open-source orchestrators (e.g., LangGraph, AutoGen, CrewAI), and custom vector infrastructures to validate optimal solutions for complex business workflows.

• Lifecycle Ownership: Independently own the full lifecycle of feature delivery—from alignment with product teams on agent objectives to final production deployment, guardrail reinforcement, and continuous optimization.

Qualifications

Skills & Competencies

• Production Agentic Engineering: Proven capability in translating raw LLMs/LMMs into reliable, stateful, and autonomous software applications with deterministic guardrails.

• LLMOps & Agent Evaluation: Deep expertise in lifecycle management for generative models, including fine-tuning, retrieval-augmented generation (RAG) optimization, prompt versioning, and routing architectures.

• Distributed State & API Design: Strong distributed systems architecture skills, specifically in managing asynchronous workflows, message queues, and low-latency API microservices required for multi-agent coordination.

• Cognitive Telemetry: Proficiency in establishing monitoring frameworks for tracking agent reasoning paths, tool execution success rates, API cost metrics, and user-intent alignment.

• Technical AI Leadership: Ability to execute rapid prototyping with emerging AI stacks, benchmark foundation models, evaluate vector databases, and drive cross-functional alignment.

Qualifications

• Experience: 6–9 years of professional experience in software engineering and machine learning development, with recent hands-on experience deploying LLM-based or agentic systems into production.

• Track Record: A proven history of successfully building, productionizing, and maintaining large-scale Machine Learning solutions and scalable backend architectures.

• Education: Degree in Computer Science, Artificial Intelligence, Data Science, or a related quantitative field.

Additional Information