Industry/Sector

Specialism

Management Level

Job Description & Summary

The Opportunity

Join our Acceleration Center India and help shape the future of business for our diverse client portfolio across geographies and jurisdictions. You’ll work at the heart of global teams across Advisory, Assurance, Tax and Business Services—solving real client challenges through connected collaboration. We’ll help you grow your skills so you can go further. With hands-on learning, cutting-edge tools and an inclusive culture, this is your opportunity to do inspiring work that makes a difference—every day.

As an AI Engineer -Senior Associate you will support regulatory risk compliance work within our Risk and Compliance practice, using AI and data-driven methods to help clients navigate complex regulatory requirements and strengthen internal controls. As a Senior Associate you will analyze complex problems, manage assigned workstreams, and develop deliverables while building meaningful client connections, mentoring junior team members, and growing your technical skills in a setting that values critical thinking and sound judgment. In this role at PwC, you will interpret data, apply a range of tools and methods to solve problems, and contribute to client service engagements that call for adaptability, collaboration, and a clear understanding of how your work supports broader business goals.

Responsibilities

- Design and implement Generative AI solutions leveraging large language models, Retrieval-Augmented Generation architecture patterns, semantic search, knowledge graphs, and enterprise knowledge integration.

- Implement Agentic AI solutions, including autonomous AI workflows, multi-agent orchestration, agentic frameworks, tool integration, guardrails, and human-in-the-loop controls for enterprise use cases. Establish prompt engineering, prompt management, evaluation, versioning, reuse, and lifecycle practices for scalable Generative AI delivery.

- Implement responsible AI and AI governance practices, including AI risk assessments, model explainability, transparency, validation, monitoring, and regulatory readiness for AI systems. Drive adoption of DevOps and MLOps practices, including CI/CD, infrastructure as code, automated testing, model deployment automation, monitoring, alerting, and release governance.
- Analyzing client regulatory and compliance workflows to identify opportunities for AI-enabled improvement.
- Developing AI solutions that support risk monitoring, regulatory reporting, and compliance review activities
- Translating complex business needs into practical technical requirements for automation and model-based tools
- Validating data sources and output logic to support accurate insights for compliance-focused decision making
- Applying AI fluency, data analysis, and digital tooling to streamline operational tasks and reduce manual effort
- Collaborating with client teams to assess control gaps, workflow bottlenecks, and emerging compliance needs
- Reviewing technical deliverables for alignment with regulatory risk requirements, governance expectations, and firm standards
- Supporting design discussions for AI-human collaboration use cases across managed services environments
- Documenting solution assumptions, limitations, and implementation considerations for technical and client stakeholders
- Mentoring junior team members on AI concepts, analytical methods, and practical application in compliance settings

What You Must Have

- At least a Bachelor's degree
- At least 5 - 8 years of experience
- Oral and written proficiency in English required

- Degree Preferred: B.E/B.Tech

- Python: Strong hands-on Python experience, including async (asyncio/anyio), type hints, and Pydantic v2. Experience building FastAPI services with versioned API contracts.

- LLM application development: Hands-on experience building LLM applications with Pydantic AI or similar frameworks. Includes typed, schema validated outputs and handling malformed model responses through retries, repair, and fallbacks.

What Sets You Apart

- Document AI: Extracting data from PDFs, scans, images, and Word files using OCR (Tesseract, PaddleOCR) and PDF tooling (PyMuPDF, pypdf). Includes deciding when to use native text and when to use OCR. LLM evaluation: Building ground-truth datasets, field-level scoring, baseline comparisons, and CI regression gates, so prompt and model changes are measured, not guessed.

- Prompt engineering: Designing, versioning, and tuning prompts for accuracy, token cost, and latency. LLM security: Mitigating prompt injection, handling untrusted input safely, and returning safe error responses. Production delivery: A track record of shipping GenAI systems to production. Sound judgment on trade-offs between quality, latency, cost, and scalability, including batching, retries, confidence thresholds, and routing to human review.

- Docker and Kubernetes (Helm, GitOps), plus CI/CD with GitHub Actions. Integrating with enterprise workflow platforms such as Appian, or other downstream systems with strict API contracts.
- Demonstrating AI Fluency and AI-Human Collaboration in client delivery
- Applying Analytical Thinking to complex regulatory risk scenarios
- Interpreting Data Analysis and Interpretation for actionable recommendations
- Navigating ambiguity while managing multiple client workstreams
- Responding to diverse perspectives with Active Listening and Empathy
- Upholding Compliance and Governance through technical diligence

Travel Requirements

Job Posting End Date