About The Position

At Codvo, we are committed to building scalable, future-ready data platforms that power business impact. We believe in a culture of innovation, collaboration, and growth, where engineers can experiment, learn, and thrive. Join us to be part of a team that solves complex data challenges with creativity and cutting-edge technology. You will own the vision, roadmap, and delivery of intelligent, AI-native products & solutions that turn complex enterprise workflows into simple, assistive experiences. You’ll operate at the intersection of product strategy, applied AI/ML, Generative AI and platform engineering - translating cutting-edge capabilities into measurable business value.

Requirements

  • 2+ years building or shipping AI/ML/GenAI products & solutions (NLP, RAG/semantic search, recommendations, predictive, or automation).
  • Practical exposure to LLMs, vector databases, prompt engineering, and cloud AI services (well familiar with Azure OpenAI, AWS Bedrock, GCP Vertex).
  • Proven agile delivery using Jira/ Confluence/ Azure DevOps; design collaboration in Figma.
  • Strong systems thinking with the ability to simplify complex workflows.
  • Strong academic background + MBA (Tier-1) engineering/CS preferred.

Nice To Haves

  • Experience with agentic systems (tool-use, function calling, planners), prompt orchestration, and knowledge retrieval frameworks.
  • Familiarity with enterprise integrations, APIs, and event-driven architectures.
  • Understanding of MLOps/LLMOps (evaluation harnesses, offline/online tests, drift monitoring, canary releases).

Responsibilities

  • Define the Vision & Roadmap
  • Own a problem-first roadmap for AI-powered platforms and assistants across priority journeys.
  • Comprehensive market research & prioritize use cases with clear value hypotheses (efficiency, revenue, quality) and Revenue Roadmap.
  • UAT first development
  • Run structured discovery (persona, journey mapping, workflow time-and-motion).
  • Author detailed UAT document for each feature with detailed acceptance criteria, test cases and evaluation metrics.
  • Product Leadership
  • Lead backlog, sprint goals, and release planning with AI/ML, data, and platform engineering.
  • Manage dependencies (data readiness, NFRs, MLOps, security, integrations) and unblock teams rapidly.
  • Measure, Learn, Iterate
  • Define north-star & guardrail metrics: accuracy, latency, cost per request, containment, adoption, and satisfaction.
  • Drive experimentation (A/B), prompt/model iterations, UX refinements, and policy updates.
  • Bridge Business & Technology
  • Translate model behavior and system constraints into business-speak (value, risk, ROI).
  • Create readable artifacts: PR/FAQ, release notes, change logs, and exec updates.
  • Safety, Compliance & Observability
  • Partner with Security/Legal to enforce PII/PHI handling, data residency, SOC2/ISO controls.
  • Ensure traceability (prompt versioning, dataset lineage, evaluation reports) and human-in-the-loop in product thinking wherever needed.
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