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.

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 contemporary 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
- Team Management & Mentorship
- Stakeholder & Client Management

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.

📌 Senior Data & AI Architect (Hyderabad)
🏢 Prudent Technologies and Consulting
📍 Hyderabad