AI Lead

Whitefield Careers; Dadri; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Whitefield Careers seeks a hands-on Technical Program Lead to design and deliver enterprise GenAI/SLM solutions, including air-gapped, on-prem, and sovereign deployments. You will own architecture end-to-end — model selection, infra, deployment, and governance — while leading delivery and the client relationship.

You will architect across hybrid environments, define AI governance, and drive engagement from discovery through to shipment.

Qualifications

• 8+ years in software/data engineering and 3+ years architecting production ML/GenAI solutions.
• Hands-on with SLMs/LLMs, fine-tuning (LoRA/QLoRA), quantization; Python, LangChain/LlamaIndex, vLLM/Ollama.
• Experience with air-gapped or on-premise AI deployment.
• Knowledge of vector DBs deployable offline (FAISS, Milvus, Weaviate, Qdrant).
• Familiarity with AI governance frameworks (NIST AI RMF, ISO/IEC 42001) and data residency requirements.
• Docker/Kubernetes and infra-as-code (Terraform/Ansible).
• Claude-driven development as core tooling; agents in the build workflow.
• Reviewer-first approach: design and review AI artifacts, not hand-coding everything.

Responsibilities

• Architect GenAI/SLM solutions across security-sensitive environments.
• Evaluate SLMs vs LLMs for cost, latency, accuracy trade-offs.
• Design air-gapped/offline deployments with local inference pipelines.
• Architect across hybrid environments (cloud and on-prem) for cost/performance.
• Define AI governance, guardrails, audit logging, and offline monitoring.
• Lead client discovery workshops and translate requirements into a delivery roadmap.
• Plan and allocate workstreams; sequence delivery to client timelines.
• Maintain strong client interaction through all project phases to shipment.
• Oversee multiple projects/accounts concurrently with proactive risk handling.
• Lead engineers/data scientists and conduct reviews to unblock delivery.
• Support pre-sales: scoping, estimation, and technical proposals.

Skills

Architectural design

Team leadership

Client-facing communication

Agile/Scrum

Tools

Claude-driven development

LangChain

LlamaIndex

vLLM/Ollama

Docker/Kubernetes

Terraform/Ansible

FAISS/Milvus/Weaviate/Qdrant

Job description

We are looking for a hands-on Technical Program Lead to design and deliver enterprise GenAI/SLM solutions, including air-gapped, on-prem, and sovereign deployments. You will

own architecture end-to-end — model selection, infra, deployment, and governance — while

leading delivery and the client relationship.

Key Responsibilities

• Architect GenAI/SLM solutions (RAG, agentic workflows, fine-tuning/distillation) suited to customer security and data-sensitivity constraints.
• Evaluate SLMs vs. LLMs (Phi, Mistral, Llama, Qwen, etc.) on cost, latency, and accuracy trade-offs.
• Design air-gapped/offline deployments — local inference, vector stores, and secure model/data update pipelines with no external dependency.
• Architect across hybrid environments: AWS/Azure/GCP, private cloud, and on-prem data centers, optimizing GPU/CPU cost and performance.
• Define AI governance: model evaluation, guardrails, audit logging, and responsible-AI practices — including offline-compatible monitoring for restricted environments.
• Lead client discovery workshops, translate business requirements into a scoped delivery roadmap, and drive the engagement through to shipment/go-live.
• Own planning and task allocation across the team — break architecture into workstreams, assigned to the right engineers, and sequence delivery against client timelines.
• Be the primary point of client interaction throughout the engagement — status updates, scope changes, escalations — not just at kickoff/handoff.
• Drive multiple projects/accounts in parallel, balancing priorities across engagements and flagging capacity or scope risk early.
• Lead a team of engineers/data scientists — planning, reviews, and unblocking delivery.
• Support pre-sales: scoping, estimation, and technical proposals.

Required Skills & Experience

• 8+ years in software/data engineering, 3+ years architecting production ML/GenAI solutions.
• Hands-on with SLMs/LLMs, fine-tuning (LoRA/QLoRA), quantization; Python, LangChain/LlamaIndex, vLLM/Ollama.
• Proven experience with air-gapped or on-premise AI deployment.
• Vector DBs deployable offline (FAISS, Milvus, Weaviate, Qdrant).
• Familiarity with AI governance/compliance frameworks (NIST AI RMF, ISO/IEC 42001) and data residency requirements.
• Docker/Kubernetes and infra-as-code (Terraform/Ansible).
• Expert in Claude-driven development — using Claude Code and Claude-based agents as a core part of the build workflow, including authoring custom Skills/MCP tools and agentic coding pipelines to boost team engineering productivity.
• Reviewer-first mindset: with agents doing most of the generation, your value is in specifying correctly, critically reviewing AI-generated architecture/code, catching subtle design and security flaws, and validating trade-offs — not in hand-writing every line yourself.

Behavioural & Leadership Expectations

• Must have: prior experience leading a small team (formally or as a de facto lead) and working across multiple clients/engagements simultaneously — this is not a first team-lead or first multi-client role.
• Leads a team end-to-end; owns the client relationship from requirement gathering through shipment.
• Spends more time planning, allocating, and reviewing than hand-coding — sets direction, defines specs/guardrails for agentic tooling, allocates tasks across the team, and audits output; comfortable being judged on decision quality and delivery outcomes, not lines of code written.
• Able to run multiple projects/accounts simultaneously without losing quality of client interaction on any one of them.
• Fluent in Agile/Scrum ceremonies; hands-on with JIRA/Confluence for backlog and delivery tracking.
• Self-driven, strong client-facing communicator across technical and non-technical stakeholders.
• Preferred: background in an IT/consulting services company over purely captive/product environments.

Good to Have

• Big Data (Spark/Hive/Hadoop), Graph Analytics, or hardware acceleration (GPU/FPGA) experience.
• Regulated-industry (defense, government, BFSI) AI deployment experience.
• Cloud, security, or AI governance certifications.
• Contribution to open source projects, academic papers published, filled patents.