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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in leading the technical vision and architecture for AI products, with a strong focus on LLMOps and MLOps practices. Proven ability to mentor teams, drive delivery, and implement scalable AI solutions in regulated environments.
Highest-signal resume keywords
LLMOps/MLOps PracticesPython ProgrammingCloud-Native Foundations (Azure, AWS, GCP)AI Product EngineeringTeam Leadership and Mentorship
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Production DeploymentsAPI/Service DevelopmentCode ReviewsContinuous Integration/Continuous Deployment (CI/CD)Prompt EngineeringAgent EvaluationData Privacy/Security-by-DesignMulti-Step Agent DesignReference ArchitecturesModel Governance
Soft Skills
Stakeholder CommunicationMentoringCoachingCulture Building
Tools & Technologies
DataBricks AIBIDataIku AIContainersOrchestration ToolsAutomated Testing
Certifications & Qualifications
Master's Degree in Computer Science or Related Field
Industry Keywords
Artificial IntelligenceMachine LearningData ScienceRegulated EnvironmentsHuman-in-the-Loop (HITL)
Tech Stack
Tools & technologiesAWSAzureCloudGoogle Cloud PlatformPythonSDLC
About the role
Key responsibilities & impact- Define and lead the technical vision, architecture, and engineering execution for Commercial AI and Agentic AI products in India
- Build and scale enterprise-grade LLM-powered and agent-based solutions from prototyping through production deployment
- Own reference architectures covering RAG, orchestration, tool integration, identity, and observability
- Lead end-to-end delivery from discovery through design, build, deployment, monitoring, and iteration
- Engineer and ship agentic workflows with planning, tool calling, reflection, memory, and guardrails
- Establish LLMOps/MLOps practices including versioning, evaluation, rollout/rollback, monitoring, and continuous improvement
- Build reusable templates, SDKs, integration patterns, and deployment scaffolds across commercial products and teams
- Drive governance, risk controls, responsible AI, privacy, auditability, and human-in-the-loop controls
- Implement LLM and agent evaluation and quality frameworks
- Own observability, tracing, cost and usage dashboards, incident triage, postmortems, and reliability improvements
- Optimize latency, quality, and cost through caching, prompt/RAG tuning, model routing, and benchmarking
- Translate commercial problems into AI roadmaps, measurable outcomes, and scalable platform components
- Align solutions with SDLC and validation expectations for regulated environments
- Mentor and lead engineers and data scientists through design reviews, coding standards, coaching, hiring input, and culture building
Requirements
What you’ll need- Master's degree (or equivalent) in Computer Science, Artificial Intelligence, Data Science, Machine Learning, Statistics, Engineering, or a related quantitative discipline with strong focus on AI/ML and modern data systems
- 8+ years of relevant experience across software engineering, ML engineering, MLOps/LLMOps, or AI product engineering
- Hands-on experience with production deployments at scale
- 4+ years leading teams and driving delivery across multiple stakeholders
- Proven ability to mentor and raise engineering standards
- Strong hands-on proficiency in Python, API/service development, Git, code reviews, and CI/CD
- Proven experience implementing LLM applications and/or AI agents in production, including evaluation and monitoring
- Strong cloud-native foundation across Azure, AWS, or GCP, including containers, orchestration, automated testing, release discipline, and operational excellence
- Capability to establish reusable frameworks/SDKs, integration patterns, and scalable operating models for AI delivery
- Excellent stakeholder communication with technical and non-technical audiences
- Hands-on experience designing multi-step agents with planning, tool/function calling, orchestration, and reflection loops
- Understanding of memory patterns, grounding strategies, and failure-mode handling
- Ability to implement guardrails, policy enforcement, prompt injection defenses, tool-use constraints, HITL checkpoints, and safe fallbacks
- Expertise in agent evaluation, trajectory analysis, tool-call accuracy, safety checks, and regression automation
- Practical knowledge of prompt engineering, structured outputs, reliability techniques, RAG, embeddings, vector databases, fine-tuning approaches, and model selection
- Experience with DataBricks AIBI, DataIku AI, or other enterprise AI engines
- Experience defining reference architectures and integrating AI/agent services with enterprise applications/APIs, authentication/authorization, logging, and audit trails
- Working knowledge of model governance, documentation, validation, monitoring, auditability, and human oversight in regulated environments
- Data privacy/security-by-design knowledge, including access controls, encryption, secure secrets, and InfoSec collaboration
Benefits
Comp & perks- Hybrid work arrangement
- Regular employee status