ML Architect

Egen

ML Architect setting technical direction for complex Generative AI projects at Egen. Collaborating directly with clients and mentoring engineering teams.

Posted 7/24/2026full-timeHyderabad • 🇮🇳 IndiaSeniorLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in architecting and deploying complex GenAI and agentic systems, with a strong focus on MLOps, production readiness, and client engagement. Proven ability to mentor and elevate engineering teams while ensuring high standards in AI practices.

Highest-signal resume keywords

Technical LeadershipGenAI DeploymentMLOps DisciplineClient-Facing ExperienceArchitecture Ownership

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonMachine LearningNeural NetworksFoundation-Model Fine-TuningEmbedding Fine-TuningAdvanced RAGSemantic SearchMulti-Agent SystemsArchitectural DesignProduction Engineering

Soft Skills

MentorshipClient EngagementTechnical CommunicationStrategic ThinkingProblem Solving

Tools & Technologies

Google CloudVertex AILangChainLlamaIndexPineconePgvector

Certifications & Qualifications

Bachelor's Degree in Computer ScienceMaster's Degree in Engineering

Industry Keywords

AI SystemsAgentic WorkflowsObservabilityResponsible AIProduction Environments

Tech Stack

Tools & technologies

CloudPython

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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.

Benefits

Comp & perks

Health insurance
Retirement plans
Paid time off
Flexible work arrangements
Professional development