Back to board
prudentglobaltech·3 months ago

Senior Data & AI Architect

Hyderabad, IndiaHybridFull-timeSenior · 15+ years

About this role

Senior Data & AI Architect
Data Engineering, AI/ML Platforms & Agentic AI Systems
Technical Architect | Delivery Lead | Practice Builder
Role Overview
We are seeking a Senior Data & AI Architect who combines deep hands-on data engineering expertise with proven leadership in delivering enterprise-grade AI solutions. This is a hybrid technical-leadership role that spans three core mandates: (1) acting as the Technical Architect for client engagements and in-house products, (2) leading and mentoring delivery teams with end-to-end accountability for outcomes, and (3) actively building and growing our Data & AI practice.
The successful candidate will architect end-to-end data ecosystems including batch and streaming pipelines, machine learning workflows, and next-generation AI systems powered by LLMs, agentic frameworks, and the Claude ecosystem. Beyond architecture, this role carries direct ownership for project delivery, team development, capability building, presales support, and revenue growth of the practice.
Key Responsibilities
1. Technical Architect — Solutions & Architecture Design
Serve as the lead architect across client projects and in-house products, owning architectural decisions from discovery through production.
Data Architecture & Platform Design
  • Design and implement scalable, secure, and high-performance data architectures (data lakes, lakehouses, warehouses) on AWS, Azure, or GCP.
  • Define data modelling standards including dimensional modelling, data vault, and domain-driven design.
  • Architect batch and real-time data pipelines using Apache Spark, Apache Kafka, and modern streaming technologies.
  • Establish data governance, quality, lineage, and compliance frameworks across systems.
  • Design cost-efficient, scalable storage and compute layers with FinOps discipline.
Data Engineering & Pipelines
  • Architect and oversee ETL/ELT workflows using orchestration tools like Apache Airflow, Dagster, or Prefect.
  • Design scalable ingestion pipelines for structured, semi-structured, and unstructured data.
  • Define CI/CD and infrastructure-as-code standards using Terraform, GitHub Actions, or equivalent.
  • Set containerization and orchestration standards using Docker and Kubernetes.
Machine Learning & MLOps
  • Architect ML pipelines covering training, evaluation, deployment, monitoring, and retraining workflows.
  • Design feature stores, model registries, and model lifecycle pipelines.
  • Integrate ML systems with production-grade data platforms and observability stacks.
Generative AI, LLM & Agentic AI Systems
  • Architect production-grade AI solutions using OpenAI, Anthropic Claude, and open-source models such as LLaMA, Mistral, and Qwen.
  • Design and deploy RAG (Retrieval-Augmented Generation) pipelines for enterprise knowledge systems, including hybrid search, re-ranking, and evaluation frameworks.
  • Architect agentic AI systems and multi-agent workflows using LangChain, LangGraph, CrewAI, Agno, and similar frameworks — with proven experience taking agentic systems from prototype to production.
  • Design AI agents capable of multi-step reasoning, tool use, planning, and human-in-the-loop control patterns.
  • Implement Model Context Protocol (MCP)-based architectures for tool interoperability and agent-to-agent (A2A) communication.
  • Build leveraging the Claude ecosystem — Claude API, Claude Code for agentic engineering, Cowork for desktop automation, Claude Skills, Artifacts, and MCP servers — as a first-class delivery accelerator.
  • Establish guardrails, evaluation harnesses, observability, cost controls, and AI governance for production AI systems.
Architecture Governance
  • Lead architecture reviews, design authority forums, and technical decision records (ADRs).
  • Author reference architectures, solution blueprints, and reusable accelerators for the practice.
  • Translate ambiguous business requirements into clear, scalable technical solutions.
2. Manager & Mentor — Delivery Ownership & Team Leadership
Own end-to-end delivery of projects with full accountability for schedule, quality, cost, and outcomes. Lead, mentor, and grow a high-performing team of data and AI engineers.
Delivery Accountability
  • Own delivery outcomes across multiple concurrent client engagements and internal product workstreams — accountable for scope, schedule, quality, cost, and customer satisfaction.
  • Define delivery plans, milestones, RAID logs, and governance cadences; report status to executive stakeholders and steering committees.
  • Drive day-to-day execution: sprint planning, backlog grooming, blocker removal, and escalation management.
  • Ensure adherence to engineering standards, code quality, security, and compliance gates across all deliverables.
  • Conduct post-implementation reviews and capture lessons learned into reusable practice assets.
Team Management & Mentorship
  • Lead, coach, and mentor a multidisciplinary team of data engineers, ML engineers, and AI engineers across onshore and offshore locations.
  • Set technical direction, conduct design reviews, pair on hard problems, and unblock the team on complex engineering challenges.
  • Own hiring loops: define role specs, screen candidates, design technical assessments, and conduct architecture interviews.
  • Drive individual development plans, performance reviews, career pathing, and skill uplift programs.
  • Foster a culture of engineering excellence, psychological safety, knowledge sharing, and continuous learning.
Stakeholder & Client Management
  • Act as the primary technical point of contact for client executives, product owners, and engineering counterparts.
  • Run discovery workshops, technical deep-dives, and architecture walkthroughs with client stakeholders.
  • Manage expectations, negotiate scope, and resolve technical disagreements with diplomacy and rigour.
3. Practice Builder — Capability & Business Growth
Build and grow the Data & AI practice through capability development, thought leadership, presales support, and direct contribution to revenue growth.
Practice Development
  • Define and evolve the Data & AI practice strategy, service offerings, and go-to-market propositions across Data Engineering, MLOps, GenAI, and Agentic AI.
  • Build a library of reusable IP — reference architectures, accelerators, frameworks, playbooks, demo assets, and POC templates.
  • Establish capability roadmaps, certification paths, and partner alignment with hyperscalers (AWS, Azure, GCP) and platform vendors (Databricks, Snowflake, Anthropic, OpenAI).
  • Develop and run internal upskilling programs, brown-bag sessions, hackathons, and certification drives to grow team capability.
  • Track and adopt emerging technologies — agentic AI frameworks, MCP, fine-tuning techniques, multimodal models — and translate them into client-ready offerings.
Business Growth & Presales
  • Partner with sales and account leaders to identify, qualify, and pursue Data & AI opportunities.
  • Lead solution shaping, proposal writing, estimation, and SOW definition for client pursuits.
  • Present in client pitches, executive briefings, and technical evaluations — translating technical depth into business value.
  • Build and maintain demo environments, proof-of-concept assets, and reference implementations that accelerate deal cycles.
  • Contribute to revenue targets through pipeline support, deal conversion, and farming of existing accounts.
Thought Leadership
  • Author whitepapers, blog posts, conference talks, and case studies that elevate the practice brand.
  • Represent the practice at industry events, meetups, and partner forums.
  • Build relationships with platform vendors, analysts, and the broader Data & AI community.
Required Qualifications
Education & Experience
  • Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
  • 15+ years of overall IT experience, including 8+ years in data engineering and 3+ years in a Data Architect or equivalent senior architecture role.
  • Demonstrable experience leading and delivering enterprise-scale data and AI projects end-to-end.
  • Proven track record of managing teams of 5+ engineers across multiple concurrent workstreams.
Core Technical Skills
  • Strong hands-on experience with distributed data processing (Spark), streaming systems (Kafka), and workflow orchestration (Airflow or equivalent).
  • Deep expertise in data modelling, data warehousing, lakehouse architectures, and modern big-data ecosystems.
  • Production experience designing and deploying ML pipelines, including model serving, monitoring, and retraining.
  • Solid experience with containerization and orchestration (Docker, Kubernetes) and CI/CD (Terraform, GitHub Actions, or equivalent).
  • Proficiency in Python and SQL; Scala is a plus.
  • Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP) at architecture level.
Agentic AI & LLM Production Experience (Required)
  • Demonstrable production experience with agentic AI frameworks (LangChain, LangGraph, CrewAI, Agno, or equivalent) — including at least one agentic system taken from design to live production deployment.
  • Hands-on experience building and operating RAG pipelines, vector databases (Pinecone, Weaviate, Qdrant, ChromaDB, or FAISS), and semantic search systems in production.
  • Strong working knowledge of LLM application patterns: prompt engineering, tool use, function calling, structured outputs, evaluation, and guardrails.
  • Experience with the Claude ecosystem — Claude API, Model Context Protocol (MCP), Claude Code, Cowork, Claude Skills, and Artifacts — applied to real client or product workloads.
  • Familiarity with multi-agent orchestration, agent-to-agent (A2A) communication patterns, and human-in-the-loop control mechanisms.
Leadership & Delivery
  • Proven experience owning end-to-end delivery of complex technical programs, including budget, schedule, and quality accountability.
  • Demonstrated success mentoring and growing engineering talent; comfortable with people management responsibilities.
  • Strong stakeholder management skills — comfortable presenting to C-level executives and negotiating with senior client leaders.
  • Track record of contributing to practice growth, presales, or business development in a consulting or product environment.
Preferred Qualifications
  • Experience fine-tuning open-source LLMs (QLoRA, LoRA, full fine-tuning) using frameworks like Unsloth, TRL, or Hugging Face.
  • Exposure to MLOps and LLMOps platforms (MLflow, Kubeflow, LangSmith, Langfuse, Weights & Biases).
  • Experience with real-time analytics, event-driven architectures, and streaming AI use cases.
  • Cloud architect certifications (AWS Solutions Architect, Azure Solutions Architect Expert, GCP Professional Cloud Architect).
  • Vendor certifications: Databricks Generative AI Engineer, Snowflake SnowPro, Anthropic / OpenAI specializations.
  • Experience with private and on-premise LLM deployments for regulated industries (financial services, healthcare, public sector).
  • Prior consulting, systems integrator, or boutique AI services background with exposure to multiple client industries.
Key Competencies
  • Architectural Thinking — ability to design coherent, scalable, secure systems that balance pragmatism and long-term evolvability.
  • Delivery Ownership — personal accountability for outcomes; bias toward action; comfort with ambiguity.
  • Leadership & Mentorship — ability to inspire, develop, and retain top engineering talent.
  • Commercial Acumen — understands the economics of consulting delivery, deal pursuit, and practice P&L drivers.
  • Communication — translates complex technical concepts into clear, persuasive narratives for executives, clients, and engineers.
  • Continuous Learning — stays at the frontier of AI and data engineering; brings new ideas into the practice.
  • Problem Solving — structured thinker who decomposes ambiguous problems and drives them to resolution.
What Success Looks Like in This Role
Within the first 6 months, the Senior Data & AI Architect will have:
  • Established themselves as the technical authority on Data & AI engagements across the portfolio.
  • Successfully delivered at least one flagship Data or AI program end-to-end with measurable client business impact.
  • Built and grown a high-performing delivery team, with clear role definition, development plans, and a strong engineering culture.
  • Contributed materially to pipeline and revenue growth through presales support, proposals, and client expansion.
  • Shipped a portfolio of reusable practice assets — reference architectures, accelerators, demos, and playbooks — that shorten future delivery cycles.
  • Positioned the practice as a credible voice in the Agentic AI and modern data platform space through thought leadership and partner engagement.

Similar jobs