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.
Whether you're producing thought-provoking research, identifying compelling investment opportunities, infusing current technologies into our business, or providing thoughtful advice to clients, we're looking for unique voices to help lead us forward. Data Engineering team under Center of Excellence (Data Science)
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.
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.
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.
Object-oriented and asynchronous Python
Data modeling, contracts, quality, reconciliation, metadata, and lineage
Workflow orchestration, distributed processing, and production operations
Experience with cloud data and AI platforms such as Azure, AWS, Google Cloud, Snowflake.
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.
Document intelligence and multimodal processing.
Experience With One Or More Of The Following
Azure OpenAI, OpenAI APIs, Anthropic Claude, Google Gemini, or Vertex AI.
Containerized model deployment and GPU-based inference.




Model serving, API gateways, quantization, memory optimization, and model configuration.
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.
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.
Governed development supported by automated testing, peer review, security scanning, and controlled deployment.
Candidates must be able to independently validate AI-generated code for:
Security and data-access risks
Performance and scalability
Dependency, licensing, and software supply-chain concerns
Evaluation, Observability, and Testing: Hands-on experience with data-quality, model-evaluation, LLM-evaluation, or AI-observability frameworks.
Experience building:
Data-quality and reconciliation tests
Continuous validation and regression testing
Production monitoring for data, retrieval, model, and agent quality
Data accuracy, completeness, freshness, and consistency
Familiarity with OpenTelemetry, OpenInference, Datadog, Arize, LangSmith, MLflow, Microsoft Foundry evaluation capabilities, or equivalent tools.

📌 Tech / AI Specialist (Pune)
🏢 Alliancebernstein
📍 Pune