Principal AI/ML Architect

Egen

. Set enterprise AI architecture and technical direction for complex GenAI, agentic AI, and ML platforms .

Posted 10/23/2026full-timeHyderabad • IndiaLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates extensive expertise in AI architecture and technical direction, with a strong focus on generative AI, machine learning, and production systems. Proven ability to lead cross-team initiatives, establish responsible AI standards, and communicate effectively with executive stakeholders.

Highest-signal resume keywords

AI Architecture DesignGenerative AI DeploymentPython ProgrammingGoogle Cloud / Vertex AITechnical Leadership

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningData EngineeringSQLNeural NetworksEmbedding ModelsModel Fine-TuningCI/CD PracticesAPI DesignDistributed SystemsEvaluation Harnesses

Soft Skills

Communication SkillsMentoringProblem FramingDecision-MakingClient-Facing Experience

Tools & Technologies

Google Cloud Vertex AILangChainGoogle Agent Development KitKubernetesEvent-Driven Architectures

Certifications & Qualifications

Google Cloud Professional Machine Learning EngineerGoogle Cloud Professional Cloud Architect

Industry Keywords

Responsible AIObservabilitySecurityPrivacy GovernanceHigh-Stakes Domains

Tech Stack

Tools & technologies

AWSAzureCloudKubernetesPythonSDLCShell ScriptingSQL

About the role

Key responsibilities & impact

Set enterprise AI architecture and technical direction for complex GenAI, agentic AI, and ML platforms
Define reference architectures, reusable patterns, engineering guardrails, and technology choices across engagements
Stay hands-on with foundation-model and embedding fine-tuning, advanced RAG, semantic search, evaluation harnesses, and agentic workflows using Python and Google Cloud Vertex AI
Design AI systems for latency, reliability, security, cost, scalability, and maintainability
Apply MLOps and LLMOps practices for versioning, evaluation, monitoring, deployment, and continuous improvement
Design and operate multi-step reasoning and multi-agent workflows using ADK, LangChain, MCP, and custom agent frameworks
Establish evaluation, observability, and responsible AI standards covering accuracy, groundedness, safety, drift, latency, and cost
Embed privacy, security, governance, and responsible AI throughout the lifecycle
Design production-scale retrieval and knowledge systems spanning ingestion, chunking, metadata, embeddings, vector and hybrid search, re-ranking, provenance, permissions, and content refresh
Partner with client executives and engineering leaders on AI roadmaps and solution architectures
Lead discovery, technical workshops, estimates, trade-off discussions, and pre-sales solutioning
Raise engineering capability through architecture reviews, design documents, code reviews, mentoring, reusable accelerators, and standards
Guide initiatives from problem framing and experimentation through production rollout, operational readiness, adoption, and measurable business value
Surface risks early and drive decisions to resolution

Requirements

What you’ll need

Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related technical field; equivalent practical experience will also be considered
15+ years of progressive experience across software engineering, machine learning, data, or AI, including substantial ownership of production systems at scale
Demonstrated principal-level technical leadership, including architecture ownership, cross-team technical direction, and decision-making across complex engagements or product portfolios
Proven record of deploying generative AI, agentic AI, and/or applied-ML products into production beyond proofs of concept
Deep hands-on expertise with Python, modern LLM and ML architectures, agent frameworks, Google Cloud / Vertex AI, production APIs, and scalable software systems
Experience establishing evaluation, observability, security, privacy, governance, and responsible AI controls for enterprise AI solutions
Strong data engineering and SQL knowledge, with the ability to design reliable data and retrieval foundations for AI products
Senior client-facing experience, with the communication skills to explain technical choices, risks, and business value to executive stakeholders and engineering teams
Experience training, tuning, or adapting neural networks, embedding models, or foundation models
Architecture experience in regulated or high-stakes domains such as healthcare, financial services, or the public sector
Familiarity with Google Cloud Professional Machine Learning Engineer, Professional Cloud Architect, or comparable certification
Experience building reusable AI accelerators, reference implementations, or platform capabilities across multiple clients or product teams
Pre-sales experience including discovery workshops, solution shaping, estimation, proposal development, and technical presentations
Familiarity or good to have understanding with Kubernetes, infrastructure as code, event-driven architectures, and modern data platforms
Expert-level Python, strong shell scripting, API and service design, clean tested maintainable code, distributed-system fundamentals, and full SDLC ownership
Deep expertise with Google Gemini, GPT-class models, LLaMA and other open models, prompt engineering, structured outputs, model selection, fine-tuning, and evaluation
Hands-on production experience with Google Agent Development Kit, LangChain, custom agent frameworks, tool use, Model Context Protocol, workflow orchestration, planning, memory, state, guardrails, human-in-the-loop controls, and multi-agent design
Advanced RAG, hybrid and semantic search, embeddings, re-ranking, and vector platforms such as Vertex AI Vector Search, pgvector, or Pinecone
Experience architecting highly available, scalable AI and software environments across Google Cloud / Vertex AI, AWS, and Azure, including model serving, data integration, IAM, networking, security, observability, and cost management
Strong command of LangChain, LlamaIndex, or equivalent orchestration layers
CI/CD, model and prompt versioning, experiment tracking, automated evaluation, observability, model monitoring, drift detection, rollback, and production incident response
Strong SQL and data engineering, classical machine learning, neural networks, training, tuning, and inference

Benefits

Comp & perks

Full-time employment
Hybrid work arrangement
Opportunities to work with Google Cloud, Salesforce, data, AI, and advanced technology platforms
Learning and professional development opportunities
Hands-on work on complex generative AI, agentic AI, and applied machine learning engagements
Client-facing technical leadership and pre-sales exposure