Senior Software Engineer – AI Engineering

Job Title: Senior Software Engineer – AI Engineering

About Anblicks

Position Overview

Key Responsibilities

• Own the architecture, design, development, and delivery of AI platform capabilities, taking solutions from prototype to production.
• Build end-to-end AI applications spanning frontend interfaces, backend services, APIs, data pipelines, and LLM integrations.
• Design and deploy LLM-powered agents and multi-agent workflows incorporating planning, tool calling, retrieval, and reasoning.
• Architect secure REST APIs and MCP servers to connect AI agents with enterprise applications, tools, and data sources.
• Lead the development of RAG pipelines covering document ingestion, chunking, embeddings, vector and hybrid search, reranking, and context assembly.
• Design governed AI platform capabilities, including agent registries, identity and access controls, policy enforcement, human approvals, usage monitoring, and rollback mechanisms.
• Integrate enterprise ontologies, taxonomies, business glossaries, knowledge graphs, and semantic layers into AI workflows.
• Partner with Data Engineering teams to design reliable batch and streaming pipelines, data models, and data contracts supporting AI applications.
• Develop context engineering frameworks to securely assemble and manage enterprise context across retrieval systems, APIs, semantic layers, memory stores, and tools.
• Establish LLM testing, evaluation, and observability practices, including response accuracy, groundedness, guardrails, tracing, and audit trails.
• Implement security and privacy controls covering PII detection and masking, secrets management, and fine-grained data access.
• Build reusable AI frameworks, SDKs, agent templates, MCP server scaffolds, and RAG components to accelerate engineering delivery.
• Collaborate with Product, Platform, Data Engineering, and Governance teams to translate business requirements into scalable technical solutions.
• Mentor engineers, lead architecture discussions and code reviews, and establish engineering best practices.

Required Qualifications

• Bachelor's degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience.
• 6+ years of professional software engineering experience, including end-to-end ownership of production systems.
• Strong backend development expertise in Python and/or TypeScript/Node.js using frameworks such as FastAPI, Flask, or Express.
• Experience building modern frontend applications using React, Next.js, or similar frameworks.
• Strong expertise in API architecture, development, integration, and security, including REST, GraphQL, webhooks, OAuth 2.0, and OIDC.
• Hands-on experience developing production-grade LLM applications using models such as OpenAI GPT, Anthropic Claude, Gemini, or Llama, including prompt engineering, tool/function calling, structured outputs, and streaming.
• Practical experience with RAG architectures and frameworks such as LangChain, LangGraph, or LlamaIndex, and vector search technologies such as pgvector, Pinecone, Weaviate, OpenSearch, or Snowflake Cortex Search.
• Experience with MCP and agent development frameworks such as OpenAI Agents SDK, Claude Agent SDK, Semantic Kernel, AutoGen, or CrewAI.
• Strong data engineering and data modeling skills, including SQL, semantic data modeling, and pipelines built using dbt, Airflow, Spark, Snowflake, or Databricks.
• Experience with cloud platforms such as AWS, Azure, or GCP, along with Docker, Kubernetes, CI/CD, and distributed system design.
• Proficiency with Git and AI-assisted development tools such as GitHub Copilot, Claude Code, Cursor, or Windsurf, including the ability to guide effective adoption within engineering teams.
• Strong system design, analytical, troubleshooting, communication, and technical decision-making skills.
• Ability to mentor engineers and communicate architectural decisions, trade-offs, and risks to technical and business stakeholders.

Good to Have

• Experience with knowledge graphs, ontologies, and semantic technologies such as RDF, OWL, SHACL, SPARQL, Neo4j, or Amazon Neptune.
• Familiarity with semantic layers such as Snowflake Semantic Views or dbt Semantic Layer.
• Experience building AI gateways, guardrails, and policy engines using tools such as NeMo Guardrails, Guardrails AI, Open Policy Agent, or Cedar.
• Knowledge of LLM observability and evaluation tools such as LangSmith, Langfuse, Arize Phoenix, MLflow, Ragas, or OpenTelemetry.
• Experience delivering software in regulated or highly governed enterprise environments with strict security, audit, and data privacy requirements.
• Familiarity with data catalog and governance platforms such as Collibra, Alation, Atlan, Unity Catalog, or Snowflake Horizon.
• Experience with event-driven and streaming architectures such as Kafka or Kinesis, and infrastructure-as-code tools such as Terraform.
• Demonstrated experience leading platform or product engineering initiatives from concept through production launch.
• Open-source contributions, technical publications, or personal projects involving agentic AI, MCP, RAG, or AI developer tooling.

What We Offer

• Opportunities to architect and build next-generation Generative AI and agentic AI solutions.
• Exposure to cutting-edge AI frameworks, enterprise data platforms, and cloud technologies.
• Technical ownership of impactful AI engineering initiatives for global enterprise clients.
• A collaborative engineering culture that encourages innovation, continuous learning, and technical excellence.
• Opportunities to mentor engineers and influence engineering standards and platform architecture.