Senior Artificial Intelligence Engineer

Tredence Inc.; Bengaluru; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

Tredence in Bengaluru, India seeks a Senior AI Engineer focused on Production AI Agents to partner with product, research and engineering teams for scalable AI-powered systems. You will build robust agent architectures, ensure production readiness, and drive evaluation-driven improvements in latency, throughput, and reliability.

You will collaborate on RAG pipelines, AI orchestration, observability, and cloud-native deployments, enabling reliable, production-grade AI solutions across workflows.

Qualifications

• Between 8 and 12 years of software/AI engineering experience with production-grade systems.

Responsibilities

• Design and build stateful, multi-agent AI systems for scalable insights generation and synthesis.
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Skills

Python

LLM Agents

AI/ML Engineering

Full-Stack Development

LangGraph

LlamaIndex Workflows

RAG

Observability

Docker/Kubernetes

Cloud (AWS/Azure/GCP)

Education

Bachelor’s in CS/Engineering

Tools

LangGraph

LlamaIndex

LangSmith

Arize Phoenix

PyTorch

Transformers

scikit-learn

Docker

Kubernetes

Job description

Senior AI Engineer Production AI Agents

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 GCI’s 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:

• 8 to 12 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.
• 6+ years of focused experience building and deploying AI-centric systems.
• 4+ 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