Long Description
Key Responsibilities
Strategy & Discovery
- Partner with business, product, and portfolio leaders to identify high-ROI GenAI and agentic use cases (knowledge work automation, decision support, customer service agents, code assistants, risk monitoring, etc.).
- Run AI readiness assessments: data landscape, governance, model options, risk and regulatory constraints.
- Produce solution blueprints and adoption roadmaps aligned to enterprise architecture and target operating model.
Architecture & Design
- Design end-to-end AI architectures: prompt/flow orchestration, RAG pipelines, multi-agent systems (planner/executor/critic), tool ecosystems (search, DB, APIs), vector stores, guardrails, observability, and CI/CD for ML.
- Select and integrate LLM providers (e.g., Azure OpenAI, Bedrock, Vertex, Anthropic) with model evaluation criteria (accuracy, latency, cost, safety).
- Define data pipelines for embeddings, chunking, metadata, and governed retrieval (role-based access, PII handling, geo-compliance).
- Architect safety & trust: content filtering, PII redaction, policy enforcement, jailbreak protection, and Responsible AI patterns.
- Plan for scalability, performance, and cost (caching, batching, streaming, quantization, distillation, serverless/containerized deployment).
Delivery & Engineering Leadership
- Lead engineers to implement agent frameworks (e.g., LangChain, Semantic Kernel, LlamaIndex, LangGraph) and workflow orchestration (e.g., Airflow, Durable Functions).
- Establish evaluation harnesses: golden sets, rubric scoring, hallucination tests, toxicity/PII metrics, regression suites.
- Drive MLOps/LLOps: versioning of prompts/flows, model registries, monitoring, drift detection, and feedback loops for continuous improvement.
- Ensure integration with enterprise systems (CRM, ERP, data lakes, APIs) and DevSecOps standards.
Governance, Risk & Compliance
- Implement Responsible AI and model risk management: documentation, auditability, exception management.
- Align with regional regulations and industry frameworks (e.g., PDPA, GDPR, financial services guidelines).
- Define human in the loop (HITL) and escalation paths for critical decisions.
Stakeholder Management & Change
- Translate complex AI concepts into business friendly narratives, TCO models, and OKR/KPI frameworks.
- Conduct enablement: playbooks, demos, training, and adoption programs for business units.
- Build vendor and partner relationships; evaluate POCs and coordinate pilots to production.
Long Description
Required Qualifications
- Bachelor's/Master's in Computer Science, Data/AI, Engineering, or related field (or equivalent experience).
- 7–12+ years in solution architecture, ML/AI engineering, or platform engineering, with 2–4+ years hands on GenAI/LLM solutions.
- Proven delivery of production GenAI systems (RAG, tool use, agents) at enterprise scale.
- Strong knowledge of:
- LLMs & Embeddings: model families, context management, fine tuning/adapter methods, prompt engineering.
- Agentic AI: planners, executors, memory, tool routing, multi-agent collaboration, safety and oversight.
- Data & Infra: vector DBs (CosmosDB, Pinecone, Redis, PgVector, Azure AI Search), data lakes/warehouses, microservices, APIs, containers (Docker/K8s), serverless.
- Cloud: Azure, AWS, or GCP—identity, networking, secrets, observability, and cost control.
- MLOps/LLOps: model/prompt versioning, A/B testing, monitoring, evaluation pipelines.
- Excellent communication, stakeholder engagement, and consultative problem solving skills.
Preferred (Nice To Have)
- Experience with Semantic Kernel, LangChain, LlamaIndex, LangGraph or custom orchestration libraries.
- Evaluation & Safety tooling: prompt injection detectors, redaction, policy engines.
- Experience with domain compliance (financial services, telco, healthcare, public sector).
- Hands-on with vectorization strategies, multilingual retrieval, and knowledge graph augmentation.
- GenAI UX experience: conversational design, guardrails in UI, user feedback instrumentation.
- Publications, patents, or OSS contributions in GenAI/agent systems.
Core Skills Matrix
Technical
- LLMs (open & closed source), embeddings, RAG, multi-agent design, tool calling.
- Python/TypeScript; API design; orchestration; CI/CD; cloud services; observability.
- Vector databases, chunking strategies, metadata & relevance tuning.
- Security & Responsible AI: content moderation, PII handling, policy controls.
Consulting & Leadership
- Use case discovery, value cases, ROI/TCO modeling.
