Full Stack AI Engineer

Job Description :

As an AI Engineer focused on Production AI Agents, you will partner closely with Product, Research, Engineering, and cross-functional stakeholders to design, build, and scale AI-powered systems that enhance how insights are generated and operationalized. This role emphasizes moving beyond experimentation to deliver reliable, evaluation-driven AI solutions that integrate seamlessly into real workflows. You will play a key role in shaping AI ecosystem by building robust agent architectures, ensuring production readiness, and continuously improving system performance and trust.

What you will accomplish :

- Design and build stateful, multi-agent AI systems using modern orchestration frameworks, enabling scalable and reliable workflows for insights generation and synthesis.

- Collaborate with Product, Research, and business stakeholders to translate requirements into end-to-end AI solutions, from proof of concept through evaluation and production deployment.

- Solid understanding of Retrieval-Augmented Generation (RAG), including hybrid search, re-ranking, and advanced retrieval techniques.

- Implement evaluation frameworks and pipelines (e.g., LLM-as-a-Judge, automated benchmarks) to measure system performance, reliability, and quality before and after release.

- Develop and maintain scalable backend services for high-throughput, low-latency workloads, while contributing to lightweight frontend components to deliver functional prototypes and internal tools.

- Optimize batching, streaming, caching, and request orchestration in distributed and async environments.

- Improve production systems across latency, throughput, reliability, observability, and unit economics.

- Partner with infrastructure teams to leverage GPU-enabled and cloud-native environments effectively.

- Establish monitoring, tracing, and observability practices for complex AI systems, ensuring performance, reliability, and debuggability in production.

- Develop reusable platform components, MCPs / APIs, and best practices for AI application development.

- Drive a pragmatic, evaluation-driven approach to adopting new AI technologies, balancing innovation with reliability and business impact.

- Stay current with advancements in AI (e.g., reasoning models, SLMs, prompting strategies) and apply them to improve systems and workflows.

- Partner with cross-functional teams to ensure AI solutions align with responsible AI, privacy, and security standards.

What you will bring :

- 5 - 8 years of experience in software engineering, AI/ML engineering, or full-stack development, with hands-on ownership of building and deploying production-grade applications or platforms.

- 4+ years of focused experience building and deploying AI-centric systems.

- 2+ years of hands-on experience with LLM-based agents, autonomous workflows, or multi-agent orchestration.

- Strong full-stack engineering experience, with deep expertise in Python and familiarity with TypeScript or Node.js.

- Hands-on experience with AI orchestration frameworks such as LangGraph, LlamaIndex Workflows, or similar tools.

- Solid understanding of Retrieval-Augmented Generation (RAG), including hybrid search, re-ranking, and advanced retrieval techniques.

- Experience implementing observability and tracing for AI systems (e.g., LangSmith, LangFuse, Arize Phoenix).

- Production experience with modern ML tooling and frameworks (for example: PyTorch, Transformers, scikit-learn).

- Proven experience taking AI-powered products from prototype to production with strong maintainability and operational quality.

- Proven ability to design and execute evaluation pipelines and testing frameworks to ensure reliability and reduce hallucinations.

- Experience working with APIs/SDKs from major model providers (OpenAI, Anthropic, Gemini) and open-source models.

- Experience deploying and managing services on cloud platforms (AWS, Azure, or GCP) and using containerization (Docker/Kubernetes).

- Familiarity with CI/CD pipelines and DevOps practices.

- Strong collaboration and communication skills, with the ability to work effectively across technical and non-technical teams.

Preferred Qualifications :

- Experience with Spring-based service development.

- Familiarity with big data and processing ecosystems (for example: Spark, Hadoop).

- Experience with streaming systems (for example: Kafka, Flink, Beam).

- Experience with RAG pipelines, vector stores, tool-use frameworks, and multimodal model integration.

- Exposure to GPU optimization and performance tuning (for example: CUDA, inference optimization techniques).

- Experience building conversational AI systems (intents, entities, dialog flows, and interaction design).

- Familiarity with prompt optimization tools such as DSPy.

- Proficiency with vector databases (Pinecone, Weaviate, Qdrant, pgvector).

- Exposure to voice agents or multimodal AI systems.

- Experience with graph databases (e.g., Neo4j) or GraphRAG approaches.

- Foundational knowledge of machine learning or model fine-tuning.