AI Engineering Lead

About the role

We are hiring a hands‑on AI Engineering leader to design and scale TRANZACT’s next‑generation Conversational & Composable AI stack across three pillars: Voice AI, Agentic AI, and AI‑driven Analytics.

Key Responsibilities

• Define and evolve the end‑to‑end Conversational AI platform: realtime ASR → agentic reasoning → TTS with sub‑second turn‑taking and production‑grade reliability.
• Establish scalable multi‑agent patterns (task routing, tool use, memory, human‑in‑the‑loop) and standard SDKs/templates to accelerate team adoption.
• Stand up enterprise‑grade Conversational RAG: hybrid retrieval, grounding/citation, freshness, and streaming context for voice/chat.
• Implement Responsible AI controls, observability, and governance (catalogs, lineage, model registry, policy enforcement) across the stack.
• Serve as Lead Engineer/Producer for at least one critical initiative per quarter, driving requirements → architecture → implementation → launch → post‑launch learning.

Success Profile (6–12 Months)

• Conversational AI platform reliably supports key production use cases with clear SLOs and runbooks.
• Reusable SDKs/templates and “golden paths” enable faster delivery across teams; internal adoption measurably increases.
• Enterprise Conversational RAG delivers grounded answers within voice turn‑taking budgets, with healthy eval & monitoring signals.
• Responsible AI controls and governance are standardized and auditable across Voice/Agentic/Analytics workloads.
• A critical initiative is led to launch with documented impact and a retrospective feeding the platform roadmap.

Minimum Qualifications

• 7+ years in applied ML/AI or realtime distributed systems; 3+ years leading production LLM/voice solutions.
• Proven experience building realtime voice agents (WebRTC‑class streaming or equivalent) with measurable business impact.
• Hands‑on fine‑tuning experience (parameter‑efficient and, when needed, full‑parameter): data curation, SFT, preference/reward optimization, safety tuning, and evals.
• Production RAG for conversational use cases (hybrid retrieval, reranking, caching, grounding/citation) with strong observability.
• Strong engineering in Python and PyTorch; exposure to TypeScript or similar is a plus.
• Bachelor’s degree in Computer Science required; Master’s in CS (AI focus) preferred.
• Experience with governed data/ML platforms (catalog, registry, lineage) and secure deployment patterns.
• Track record of mentoring and uplifting teams; effective communicator with stakeholders across technical and non‑technical domains.

Preferred Qualifications

• Vector/search platforms and rerankers; hybrid lexical + dense retrieval; multilingual/domain adapters.
• LLM traces/observability tooling; model registries; dataset versioning and approval gates.
• Safety tooling (policy classifiers, redaction/PII handling) and progressive rollout (shadow/canary/feature flags).
• Streaming RAG for Voice AI (incremental retrieval, early‑token generation/TTFB optimization, barge‑in‑friendly generation).
• Telephony/contact‑center systems and marketing analytics experience.