Job Description:You own the implementation end-to-end: discovery through production scale. You'll map client workflows, architect agent-based solutions on MediaMint AI, ship them live, and prove ROI with hard numbers. You also lead a team of implementation engineerssetting technical standards, unblocking production issues, and turning one-off wins into reusable playbooks. This is a hands-on leadership role: you architect, you code, you ship, you lead.

What You'll Do:

- Process Discovery & Value Sizing Run workshops and shadow operations to build current-state maps (SIPOC, BPMN, swimlanes). Identify bottlenecks, SLA risks, error sources, and compliance constraints. Size every opportunity with baselines (time, cost, error rate) and target outcomes.
- Solution Architecture Translate client workflows into MediaMint AI's modular agent stack: data ingestion, enrichment, decisioning, QA, notifications. Define guardrails, exception paths, human-in-the-loop checkpoints, and prompt strategies. Own the technical design from schema mapping through API integration.
- Hands-On Engineering – Build, configure, and deploy AI agents and automation pipelines using Python. Implement RAG patterns, prompt engineering, and LLM orchestration via LangChain, AutoGen, or CrewAI. Integrate with OpenAI API, Azure OpenAI, REST APIs, and third-party systems. Debug agent logic, data flows, and integration failures in production.
- Pilot-to-Scale Execution – Scope tight pilots with clear exit criteria. Configure agents, prompts, rules, and integrations. Coordinate UAT with Service Delivery and client SMEs. Cut over to production with dashboards, SLIs/SLOs, runbooks, and tiered support paths.
- Team Leadership – Lead a team of implementation engineers. Set coding standards, review architectures, and conduct design discussions. Mentor engineers on agentic patterns, debugging techniques, and client communication.



Ensure the team delivers reliably under pressure.
- Client-Facing Technical Ownership – Handle technical communication with clients directly. Translate business needs into engineering plans and engineering constraints into business language. Run weekly steering, RAID logs, and OKR tracking.
- Platform Feedback Loop – Convert field learnings into platform requirements and reusable templates. Influence the MediaMint AI roadmap with quantified impact data and voice-of-customer insights.

What you must bring:

- 10+ years in software engineering with hands-on AI/automation implementation experience and at least 2 years leading a technical team.
- Production experience building LLM-powered applications, AI agent systems, or agentic workflow pipelines.
- Robust Python backend skills: REST APIs, third-party integrations, API authentication (OAuth, tokens, API keys).
- Working knowledge of AI frameworks (LangChain, AutoGen, CrewAI) and platforms (OpenAI API, Azure OpenAI).
- Proven expertise in process discovery and workflow redesign—able to map a client operation and architect an automated replacement.
- Experience with automation tools (Playwright, Selenium, or equivalent).
- Strong debugging instincts: diagnosing agent logic failures, data flow issues, and integration breakdowns in production.
- Clear, concise communicator—comfortable running client workshops, steering meetings, and technical reviews.

Bonus Points:

- Domain experience in AdTech/MarTech/RevOps (campaign setup, pacing, QA, attribution, trafficking, reporting).
- Process improvement credentials (Lean Six Sigma, BPMN) or change management experience (Prosci/ADKAR).
- Exposure to data tooling (Airflow, dbt, Snowflake, BigQuery) and CRM/ticketing systems (Salesforce, Jira, Zendesk).
- Experience with cloud infrastructure (AWS/Azure/GCP), containers (Docker, Kubernetes), and CI/CD pipelines.
- Background in quality frameworks for AI: guardrails, red-teaming, bias detection, and latency SLAs.

📌 Technical Lead - AI Implementation (Telangana)
🏢 MediaMint
📍 Telangana