AVP/AI Engineer

Company Description
As a leading global investment management firm, AB fosters diverse perspectives and embraces innovation to help our clients navigate the uncertainty of capital markets. Through high-quality research and diversified investment services, we serve institutions, individuals, and private wealth clients in major markets worldwide. Our ambition is simple: to be our clients’ most valued asset-management partner.
With over 4,400 employees across 51 locations in 25 countries, our people are our advantage. We foster a culture of intellectual curiosity and collaboration to create an environment where everyone can thrive and do their best work. Whether you're producing thought-provoking research, identifying compelling investment opportunities, infusing new technologies into our business, or providing thoughtful advice to clients, we’re looking for unique voices to help lead us forward. If you’re ready to challenge your limits and build your future, join us.
Who You’ll Work With
Data Engineering team under Center of Excellence (Data Science)
What You’ll Do
AI and Data Engineer, Agentic AI Platform
We are building a next-generation enterprise AI and data platform that enables teams to design, deploy, integrate, and scale intelligent applications and agents across investment research, portfolio management, risk, client, and operational workflows.
The platform provides standardized infrastructure, reliable data products, commercial and self-hosted model access, retrieval, agent orchestration, evaluation, observability, security, and governance. It enables teams to rapidly deliver AI-powered capabilities in a scalable, secure, and production-ready manner.
We are looking for hands-on AI and Data Engineers who can:
Build reliable, reusable, and AI-ready data products for investment and enterprise use cases.
Design and develop production-grade AI applications, agents, and intelligent workflows using Python.
Implement agent orchestration, tool integration, retrieval, memory, and multi-step workflows.
Integrate AI capabilities into enterprise applications, data platforms, and business systems.
Contribute to the shared AI and data platform that powers these capabilities at scale.
This role operates at the intersection of AI engineering, data engineering, agentic AI, platform engineering, and investment technology. It emphasizes Python, enterprise integration, reliable data products, security, CI/CD, observability, model and agent evaluation, and rigorous validation of production AI systems.
What We’re Looking For
Core AI and Data Engineering
5+ years of experience in software, data, AI, or platform engineering.
Strong production development experience with Python and SQL.
Proven ability to build and operate AI applications, data pipelines, APIs, data products, and distributed systems.
Experience integrating AI capabilities into enterprise applications and business workflows.
Strong understanding of:
Object-oriented and asynchronous Python
APIs, microservices, and application integration
Data modeling, contracts, quality, reconciliation, metadata, and lineage
Batch, streaming, event-driven, and API-based architectures
Workflow orchestration, distributed processing, and production operations
Experience with cloud data and AI platforms such as Azure, AWS, Google Cloud, Snowflake.
Ability to design scalable, reusable, resilient, and operationally supportable services.
Generative AI and Agentic Systems
Hands-on Experience With
Large language models, embeddings, prompt engineering, context engineering, and RAG architecture.
Commercial and open-source model APIs and ecosystems.
AI agents, agentic workflows, reusable skills, tool calling, structured outputs, and multi-step orchestration.
Vector, keyword, metadata-filtered, graph-based, and hybrid retrieval.
Agent frameworks such as LangGraph, Microsoft Agent Framework, Semantic Kernel, AutoGen, Pydantic AI, or equivalent platforms.
Preferred Experience Includes
MCP servers and clients, agent-to-agent communication, and enterprise search.
Semantic layers, ontologies, and knowledge graphs.
Agent memory, state management, and human-in-the-loop controls.
Document intelligence and multimodal processing.
Reusable agent tools, skills, manifests, and registration standards.
Model Hosting and Inference
Experience With One Or More Of The Following
Azure OpenAI, OpenAI APIs, Anthropic Claude, Google Gemini, or Vertex AI.
Hugging Face, vLLM, Ollama, Qwen, or other approved open-source model technologies.
Containerized model deployment and GPU-based inference.
Model serving, API gateways, quantization, memory optimization, and model configuration.
Context-window, token, concurrency, batching, throughput, and caching optimization.
Benchmarking and monitoring model quality, latency, availability, resource utilization, and cost.
Platform Engineering, Security, and DevOps
Strong Experience With
CI/CD, infrastructure as code, automated provisioning, and governed deployment.
API-first and microservices architectures.
Docker, Kubernetes, AKS, GKE, or equivalent container platforms.
Authentication, authorization, secrets management, OAuth, managed identities, RBAC, and Microsoft Entra ID or equivalent technologies.
Repository governance, branch protection, pull-request validation, dependency controls, and software supply-chain security.
Metrics, distributed tracing, structured logging, monitoring, and alerting.
Designing observable, resilient, secure, and production-ready AI systems.
Experience using AI-assisted development tools such as Claude Code, GitHub Copilot, Cursor, Codex, or comparable tools for:
Code generation, refactoring, debugging, testing, and documentation.
Data-pipeline, infrastructure, and CI/CD development.
Codebase understanding, modernization, architecture extraction, and pull-request review.
Governed development supported by automated testing, peer review, security scanning, and controlled deployment.
Candidates must be able to independently validate AI-generated code for:
Functional correctness and test coverage
Security and data-access risks
Performance and scalability
Maintainability and architectural alignment
Dependency, licensing, and software supply-chain concerns
Compliance with enterprise engineering standards
Evaluation, Observability, and Testing: Required
Hands-on experience with data-quality, model-evaluation, LLM-evaluation, or AI-observability frameworks.
Experience building:
Model benchmarking and evaluation pipelines
Data-quality and reconciliation tests
Retrieval and RAG evaluations
Agent test harnesses and scenario-based simulations
Continuous validation and regression testing
Production monitoring for data, retrieval, model, and agent quality
Ability to measure:
Data accuracy, completeness, freshness, and consistency
Retrieval relevance and coverage
Groundedness, factual consistency, citations, and provenance
Tool selection, execution, and structured-output compliance
Agent workflow success, failure, and escalation behavior
Latency, throughput, reliability, resource utilization, and cost
Familiarity with OpenTelemetry, OpenInference, Datadog, Arize, LangSmith, MLflow, Microsoft Foundry evaluation capabilities, or equivalent tools.
Ability to use evaluation results to inform model selection, routing, release, rollback, and production-readiness decisions.
Location: Pune, India
Pune, India

Work arrangement
No