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Hyderabad, Telangana, India
Hybrid
About Egen
Egen is a fast-growing and entrepreneurial company with a data-first mindset. We bring together the best engineering talent working with the most advanced technology platforms, including Google Cloud and Salesforce, to help clients drive action and impact through data and insights. We are committed to being a place where the best people choose to work so they can apply their engineering and technology expertise to envision what is next for how data and platforms can change the world for the better. We are dedicated to learning, thrive on solving tough problems, and continually innovate to achieve fast, effective results. If this describes you, we want you on our team.
As a ML Architecht, you set the technical direction for our most complex Generative AI work — and you are personally hands-on building it. We treat AI as a software engineering discipline, and you are the person who raises that bar: architecting systems that survive contact with production, and elevating the engineers around you to do the same.
You own the hardest problems in our portfolio — agentic platforms operating at scale, applied-ML work, and AI in regulated, high-stakes environments where being wrong is expensive. Document intelligence and RAG pipelines are part of the toolkit, not the ceiling. You take these systems from an ambiguous business problem to a robust, scalable production service, solving for the real-world constraints — latency, reliability, cost, governance — that separate a demo from a system a client can bet on.
You are also the technical face of our engagements. You partner directly with client leadership to translate business strategy into AI architecture, shape solutions in pre-sales, and earn the trust that turns a project into a program. This role blends deep, current mastery of LLMs and ML with the judgment, communication, and consultative instinct of a senior technical leader.
Set technical direction: Own architecture for our most complex GenAI and agentic systems end-to-end, and set the standards — evaluation, observability, responsible AI — that the practice builds to.
Build at the frontier, hands-on: Stay in the code where it matters most — foundation-model and embedding fine-tuning, novel agentic workflows, advanced RAG and semantic search — using Python on Google Cloud (Vertex AI), LangChain/LlamaIndex, and vector search (Vertex AI Vector Search, Pinecone, pgvector).
Engineer for production: Design for latency, reliability, cost, and scale from day one; apply MLOps discipline so systems are served efficiently, monitored, and continuously improved — and actually reach production, where most AI work stalls.
Lead multi-step reasoning at scale: Architect and operate agentic workflows that automate complex reasoning reliably, with the design and verification discipline that keeps multi-agent systems from cascading into failure.
Advise clients and shape deals: Work directly with client leadership to understand strategy, propose state-of-the-art approaches, and shape solutions in pre-sales — the technical authority in the room.
Multiply the team: Elevate senior and mid-level engineers through architecture reviews, mentorship, and setting a high, teachable bar for AI-augmented engineering.
Core Languages: Mastery of Python and shell scripting; fluency across the modern AI engineering stack.
AI/LLM Ecosystem: Deep, current expertise with Google Gemini, GPT-class, and open models (LLaMA); advanced prompt engineering, fine-tuning, and evaluation.
Agentic AI Engineering: Hands-on experience building autonomous agents and workflows using ADK, LangChain, and custom agent frameworks.
Agentic Systems: Proven experience designing and productionizing agentic and multi-step reasoning systems (MCP, tool use, orchestration) — not just prototypes.
Data & Search: Expertise in vector databases (Vertex AI Vector Search, pgvector, Pinecone) and semantic search at production scale.
Cloud Infrastructure & AI Systems: End-to-end experience architecting high-availability, scalable software environments across cloud providers (GCP/Vertex AI, AWS, Azure).
Frameworks: Strong command of LangChain, LlamaIndex, or equivalent orchestration layers — and the judgment to know when not to reach for them.
Engineering foundation: A first-class software engineer — clean, maintainable code, full SDLC ownership, and the architectural judgment to lead others.
Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.
12+ years in software / AI / ML engineering, with a substantial track record of AI systems delivered to production at scale.
Demonstrated technical leadership — owning architecture and setting direction across engagements or teams, not just individual deliverables.
Proven track record of deploying GenAI and/or agentic products to production environments.
Experience with classic machine learning (neural nets, training, tuning) strongly preferred; foundation-model or novel-model work a distinct plus.
Senior client-facing experience — translating technical complexity into business value for executive stakeholders.
Ownership at scale: You take responsibility not just for your code, but for the outcome of the system and the success of the team building it.
Curiosity: The AI landscape changes weekly; you stay at the frontier and bring the team with you.
Consultative authority: You earn the trust of client leadership and are the calm, credible technical voice in a high-stakes room.
Ethics: You hold the line on responsible AI development and data privacy.