Mission
The primary objective is to lead the delivery and execution of AI engineering projects, ensuring the application of engineering standards, alignment with business objectives, and the delivery of measurable value through the implementation of modern AI systems.
Key Responsibilities
Support the Engineering Manager (EM) in day-to-day responsibilities.
Drive day-to-day delivery execution (planning, dependencies, unblocking, risk management) and ensure commitments are met.
Ensure engineering standards are applied (definition of done, testing strategy, code quality, documentation, operational readiness).
Provide clear engineering updates to stakeholders and escalate promptly when needed.
Enable team execution without direct line management: support onboarding and ways of working, mentor/coach engineers and tech leads, share best practices.
Ensure knowledge transfer and handover: document decisions, runbooks, and key architectural choices; facilitate smooth transition at the end of engagement.
Partner with Product/Business to identify where agentic AI provides clear value (workflow automation, assisted decision-making, content acceleration).
Translate needs into technical objectives (latency, cost, quality, robustness, compliance) and success criteria (KPIs, A/B testing, guardrails).
Design agentic architectures: tool orchestration, planning, memory management, context management, retrieval (RAG), routing, multi-agent patterns, human-in-the-loop.
Implement production patterns: prompt/versioning, evaluation harnesses, regression tests, feature flags, canary releases, and monitoring for quality/cost/latency.
Engineer for resilience: fallbacks, timeouts, retries, circuit breakers, safe tool execution, sandboxing, secrets management.
Implement guardrails: tool policies, content filtering, PII redaction, policy-as-code, access controls, auditability, and traceability (traces, conversations, decisions).
Partner with IT/Security/Cloud teams to ensure privacy, security, and risk compliance at scale.
Define and enforce quality gates before production (red-teaming, adversarial testing, bias, hallucination risk management).
Build and operate the ML value chain: data contracts, data quality, lineage, drift monitoring, dataset management, training/inference pipelines.
Own production operations for models and agentic services: deployment, scaling, observability, incident response, SLOs, post-mortems.
Industrialize continuous evaluation: offline evaluations, golden sets, human feedback loops, scorecards.
Coach tech leads and engineers on delivery habits, quality, and operational excellence (without direct people management).
Strengthen ways of working: documentation, testing, product ownership (“you build it, you run it”), and incident readiness.
Facilitate collaboration across software engineers, data engineers, data scientists, ML engineers, SRE, product, and security.
Required Experience
Proven engineering leadership delivering data/ML products in production (scalability, reliability, security).
Strong grasp of modern LLM/agentic patterns: RAG, embeddings, tool-calling/function execution, memory, evaluation, tracing.
Excellent software engineering foundations (architecture, microservices, API design, testing, CI/CD) and cloud (ideally Azure).
Hands-on experience with MLOps/LLMOps: monitoring, deployment, governance, cost/latency optimization, observability (metrics/logs/traces).
Solid data engineering knowledge (pipelines, orchestration, quality) and SRE practices (SLOs, incidents, runbooks).
Servant leadership with high standards and strong stakeholder alignment skills.
Product mindset and impact orientation (measurement, iteration, prioritization).
Ability to communicate complex topics clearly (risks, trade-offs, technical roadmap).
Comfort operating in ambiguity and turning emerging tech (agentic/LLM) into actionable plans and standards.
Nice to Have
Strong experience across software/data/ML, including significant technical leadership (people management experience is a plus but not required).
Demonstrated experience shipping ML/LLM solutions under real constraints (security, cost, performance, operations).
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