Senior Technical Architect (DPO)

Job Summary

We are seeking an experienced, high-impact AI Development Lead cum Project Manager with a minimum of 10+ years of proven industry experience to spearhead our artificial intelligence and machine learning delivery portfolio. This hybrid leadership role is designed for a technologist who bridges deep algorithmic competence with rigorous Agile project management expertise.

In this dual-capacity role, you will be equally accountable for technical architectural integrity, code quality, and MLOps pipelines (Technical Lead), as well as project scheduling, scope control, resource loading, client stakeholder management, risk mitigation, and budget adherence (Project Manager). You will guide cross-functional teams of Data Scientists, Machine Learning Engineers, Full-Stack Developers, and QA Analysts to deliver cutting-edge AI solutions on time and within budget.

Key Responsibilities

.A Technical AI Leadership & Solution Architecture (50%)

• Architectural Leadership: Lead end-to-end architecture, development, and deployment of enterprise AI systems across NLP, Computer Vision, Predictive Modeling, and Generative AI / LLM applications.
• Code Quality & Engineering Rigor: Establish and enforce coding standards, peer code review protocols, algorithmic rigor, unit/integration testing, and repository branching strategies.
• MLOps & CI/CD Pipelines: Oversee production MLOps workflows including automated model training pipelines, experiment tracking, feature stores, model registries, containerized inference, and drift monitoring.
• Generative AI & LLM Systems: Drive architectural design for Retrieval-Augmented Generation (RAG), LLM fine-tuning, prompt engineering frameworks, and agentic workflows using LangChain/LlamaIndex.
• Security & Responsible AI: Enforce AI security standards, model explainability, bias mitigation, data governance, and regulatory compliance (GDPR, HIPAA, SOC 2).

B. Agile Project Management & Delivery Execution (50%)

• Project Planning & Scope Governance: Define detailed project charters, Statements of Work (SOWs), work breakdown structures (WBS), sprint backlogs, and resource loading schedules.
• Agile Sprint Orchestration: Act as Scrum Master / Agile Delivery Lead, orchestrating sprint planning, daily stand-ups, backlog grooming, sprint reviews, and retrospectives in Jira.
• Timeline & Milestone Tracking: Track project milestones, sprint velocity, burn-down charts, and deliverables; proactively identify and resolve bottlenecks, dependencies, and scope creep.
• Budget & Financial Management: Forecast and control project financials, billable resource utilization, team allocations, software licensing costs, and cloud compute expenditure.
• Risk Management & Escalations: Maintain proactive risk registers, issue escalation matrices, and mitigation strategies to safeguard delivery deadlines and SLAs.

C. People Leadership, Mentorship & Stakeholder Management

• Team Management & Mentoring: Directly manage, coach, and upskill a high-performing cross-functional squad of 10-25 data scientists, ML engineers, backend developers, and testers.
• Stakeholder Alignment: Serve as the primary liaison between business sponsors, executive leadership, client product owners, and technical delivery teams.
• Business Translation: Translate complex business challenges into quantifiable AI user stories and technical delivery epics with well-defined acceptance criteria.
• Pre-Sales & Estimation: Collaborate with pre-sales and leadership on RFP responses, solution scoping, project estimation, and client discovery workshops.

Skill Requirements

• Total Experience: Minimum 10+ years of professional IT/software engineering experience, with at least 4+ years in a dual role combining AI technical leadership and project delivery.
• Educational Background: Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Information Systems, or equivalent engineering discipline.
• AI / Machine Learning Depth: High proficiency in Python, PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers, Pandas, NumPy, and OpenCV.
• GenAI & LLM Orchestration: Hands-on experience architecting RAG pipelines, vector databases (Azure AI Search, Pinecone, Chroma, Milvus), prompt engineering, and agentic frameworks (LangChain, AutoGen).
• MLOps & Cloud Infrastructure: Production experience with MLflow, Kubeflow, Docker, Kubernetes, CI/CD tools (GitHub Actions, GitLab CI, Azure DevOps), and automated model deployment.
• Cloud AI Ecosystems: Deep architectural familiarity with major cloud AI platforms: Microsoft Azure (Azure OpenAI, Azure ML) and/or AWS (SageMaker, Bedrock).
• Agile Delivery & Tooling: Proven track record managing projects using Scrum, Kanban, and SAFe frameworks; expert administrator in Jira, Confluence, and MS Project.
• Estimation & Financials: Demonstrated capability in project estimation (Story Points, Function Points, Three-Point Estimation), risk mitigation, and milestone tracking.
• Communication & Leadership: Exceptional executive-level communication, client relationship management, negotiation, and conflict resolution capabilities.

4. Preferred Certifications & Domain Experience

• Project Management Certifications: Project Management Professional (PMP), Certified Scrum Master (CSM), or PMI Agile Certified Practitioner (PMI-ACP).
• Cloud AI Certifications: AWS Certified Machine Learning - Specialty, AWS Solutions Architect, Azure AI Engineer Associate (AI-102), or Azure Solutions Architect Expert.
• Vertical Domain Experience: Domain expertise delivering AI initiatives in Banking, Financial Services, Insurance, Healthcare, Life Sciences, or Retail/Supply Chain.
• Contractual Governance: Prior experience managing fixed-price and time-and-materials (T&M) contracts for enterprise consulting or Fortune 500 clients.

5. Key Performance Indicators (KPIs) & Success Metrics

Success in this dual role will be measured across five foundational delivery pillars:

• 1. Delivery Predictability: Sprint Velocity & Predictability: Achieving ≥85% planned sprint commitment completion and zero critical release slippage.
• 2. Engineering Excellence: Model Quality & Production Stability: Deploying validated models meeting pre-set precision, recall, and latency SLAs in live production.
• 3. Financial Adherence: Budget & Cost Governance: Completing engagements within ±5% of allocated hours and budget, avoiding margin erosion.
• 4. Customer Trust: Stakeholder Satisfaction: Sustaining a Client Satisfaction (CSAT) score of ≥90% and positive business sponsor feedback.
• 5. Talent Growth: Team Development & Retention: Fostering team retention above 90%, with active cross-skilling across AI and software engineering.

Other Requirements