Job Description

This is a remote position.

About the Role

We are looking for an experienced Project Manager to coordinate end-to-end delivery execution, unblock client dependencies, manage delivery milestones, and ensure formal stakeholder approvals across both phases of the engagement.

The ideal candidate will have strong experience managing enterprise cloud infrastructure and AI integration initiatives within structured corporate environments, combining rigorous schedule tracking, proactive RAID log governance, and executive communication to ensure successful delivery across the 22-week programme lifecycle.

Programme Context & Phased Effort Allocation

The engagement is structured as a 22-week sequential programme divided into two delivery phases, assuming a five-day working week:

- Phase 1: MCP Gateway and Governance (Weeks 1–10 / 10 Weeks): Part-time allocation (0.5 FTE) coordinating gateway requirements, environment prerequisites, sprint plans, and Phase 1 deliverable sign-offs.

- Phase 2: Model Access through Microsoft Foundry (Weeks 11–22 / 12 Weeks): Part-time allocation (0.5 FTE) coordinating Foundry model rollouts, evaluation milestones, stakeholder acceptance, and operational handover.

Key Responsibilities

- Master Delivery Plan & Reporting: Maintain the master delivery plan, milestones, resource schedules, burn-down metrics, and executive progress reporting.

- Dependency & RAID Governance: Track access requirements, technical prerequisites, dependencies, risks, and issues, with clear named owners and resolution dates.

- Stakeholder Coordination: Coordinate client stakeholders, architecture review meetings, weekly delivery check-ins, and steering committee decisions.

- Scope & Commercial Change Control: Manage scope changes and evaluate their impact on person-week effort, timelines, deliverable commitments, and commercials.

- Acceptance & Deliverable Sign-off: Coordinate acceptance testing, release approvals, governance reviews, and formal contractual deliverable sign-offs.

- Operational Handover: Track completion of technical documentation, standard operating procedures, knowledge transfer, and operational handover.

Required Skills & Experience

- 8–12+ years of experience managing enterprise technology projects involving cloud infrastructure, API management, and integration.

- Delivery Schedule & Milestone Management: Proven track record maintaining multi-phase delivery roadmaps, resource schedules, and managing delivery burn rates.

- Rigorous RAID & Dependency Management: Strong expertise proactively identifying, tracking, and resolving technical dependencies, security access requests, and cross-functional risks.





- Stakeholder Engagement & Facilitation: Skilled at facilitating client review meetings, steering group updates, and aligning technical delivery teams with executive sponsors.

- Scope & Commercial Governance: Demonstrated experience managing Statement of Work (SOW) deliverables, milestone acceptance criteria, and formal change requests.

- Methodologies & Tools: Proficiency in Agile/Scrum and Waterfall delivery models, Jira, Confluence, and enterprise PM reporting tools.

Preferred Qualifications

- Financial Services / Capital Markets Background: Experience delivering technology initiatives within tier-1 banks, asset management, or regulated corporate environments.

- Cloud & AI Project Experience: Previous management of Azure cloud implementations, API gateway rollouts, or enterprise LLM/GenAI initiatives.

- Project Management Certifications: PMP, PRINCE2 Practitioner, or Agile/Scrum Master certifications (CSM/PMI-ACP).

- Client-Facing Consulting Experience: Prior experience in a management consulting or professional services delivery environment.

Key Competencies

- Enterprise Technology Project Delivery

- Master Milestone Planning & Resource Scheduling

- Dependency Tracking & Proactive RAID ResolutionRequirements
Key Responsibilities Develop and maintain enterprise AI applications powered by Anthropic Claude models. Design, build, and optimize inference pipelines for production AI workloads. Integrate Claude APIs into enterprise applications and backend systems. Design and implement Retrieval-Augmented Generation (RAG) solutions using vector databases. Build AI agents and agentic workflows using modern orchestration frameworks. Develop scalable backend services and REST APIs using Python. Implement prompt engineering strategies for different business use cases. Optimize AI application performance for latency, throughput, reliability, and cost. Build reusable AI components, libraries, and integration frameworks. Collaborate with cross-functional teams to translate business requirements into technical solutions. Integrate AI applications with enterprise systems, APIs, and databases. Implement monitoring, logging, evaluation, and observability for AI applications. Troubleshoot production issues and continuously improve platform performance.



Follow enterprise security, governance, and responsible AI best practices. Participate in code reviews, technical discussions, and architecture planning. Required Skills & Qualifications 6–8 years of experience in Software Engineering or Backend Development. Minimum 2 years of hands-on experience working with Large Language Models (LLMs) and Generative AI. Solid hands-on experience with Anthropic Claude APIs and enterprise AI integrations. Proficiency in Python. Experience building REST APIs using FastAPI, Flask, or similar frameworks. Strong understanding of: Large Language Models (LLMs) Prompt Engineering Retrieval-Augmented Generation (RAG) AI Agents and Agentic Workflows Embeddings and Semantic Search Experience with AI orchestration frameworks such as: Lang Chain Lang Graph Semantic Kernel Experience working with vector databases such as: Pinecone ChromaDB Weaviate Milvus FAISS Experience integrating AI applications with enterprise platforms and APIs. Good understanding of cloud platforms such as Microsoft Azure or Amazon Web Services (AWS). Familiarity with Git, Docker, Kubernetes, and CI/CD pipelines. Strong analytical, debugging, and problem-solving skills. Excellent communication and collaboration abilities. Preferred Skills Experience building AI copilots or conversational AI solutions. Experience developing multi-agent AI systems. Familiarity with Model Context Protocol (MCP). Experience with Azure AI Foundry or Azure OpenAI Services. Knowledge of AI evaluation and observability tools such as Langfuse, Phoenix, or Open Telemetry. Exposure to Redis, Kafka, or event-driven architectures. Understanding of AI guardrails, responsible AI, and enterprise security practices. Experience with Dev Ops and MLOps concepts for AI deployments. Technical Skill Set Programming Languages Python SQL AI & Generative AI Anthropic Claude Prompt Engineering Retrieval-Augmented Generation (RAG) AI Agents Lang Chain Lang Graph Semantic Kernel Backend Technologies FastAPI Flask REST APIs Microservices Databases PostgreSQL Pinecone ChromaDB Weaviate Milvus FAISS Cloud Platforms Microsoft Azure Amazon Web Services (AWS) Dev Ops & Tools Git Docker Kubernetes CI/CD Pipelines Monitoring & Observability Langfuse Open Telemetry Phoenix (Preferred) Qualifications Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field. Master's degree is an advantage but not mandatory. Preferred Certifications Azure AI Engineer Associate (AI-102) Azure Fundamentals (AZ-900) AWS Certified Machine Learning – Specialty (Preferred) Anthropic Claude Certification or equivalent Generative AI certification (Preferred)

📌 Project Manager – Cloud Infrastructure, AI & Governance Delivery (India)
🏢 LatentBridge
📍 India