Job Summary

Ciklum is looking for a Senior Artificial Intelligence/Machine Learning Engineer to join our team full-time in India.

Role Overview

As a Senior Artificial Intelligence/Machine Learning Engineer, become a part of a cross-functional development team engineering experiences of tomorrow.

We are seeking a Senior AI/ML Engineer to architect, build, and deploy end-to-end, LLM-powered agentic AI solutions for complex production use cases. In this role, you will lead the design and execution of autonomous multi-agent systems, high-accuracy Retrieval-Augmented Generation (RAG) pipelines, and advanced prompt engineering architectures.

You will bridge applied machine learning, Natural Language Processing (NLP), and robust backend engineering to move autonomous systems from experimental prototypes into reliable, observable, and enterprise-grade production environments.

Responsibilities

- Agentic AI Multi-Agent Orchestration
- Autonomous Workflows: Architect, evaluate, and deploy multi-agent coordination frameworks using LangGraph, LangChain, or CrewAI to solve complex, non-deterministic business workflows
- Agent Capabilities: Design structured planning, self-correction/reflection loops, tool-calling mechanisms, memory management, and dynamic routing architectures
- Prompt Engineering Alignment: Develop advanced prompt engineering strategies (e.g., Chain-of-Thought, ReAct, directional stimulus prompting, few-shot conditioning) ensuring low latency, structured JSON outputs, and guardrails against hallucination
- RAG Information Retrieval Architecture
- Advanced RAG Pipelines: Design, benchmark, and optimize enterprise-grade RAG systems implementing semantic search, hybrid retrieval (dense + sparse keyword matching), re-ranking, contextual chunking,



and query decomposition
- Vector Data Engineering: Configure, scale, and manage vector databases (e.g., Milvus, Pinecone, Qdrant, Chroma, PGVector, or AWS OpenSearch) for high-dimensional embedding retrieval
- NLP Embeddings: Benchmark, fine-tune, or calibrate state-of-the-art embedding models, tokenizers, and domain-adapted text representation pipelines
- Productionization, MLOps LLM Observability
- LLM Evaluation Observability: Establish rigorous evaluation frameworks (groundedness, context relevance, faithfulness) and trace agent executions using platforms such as LangSmith, Langfuse, or TruLens
- System Design API Integration: Collaborate on clean system design to serve agentic workflows via modern backend interfaces and microservices
- Continuous Improvement: Implement automated testing suites to evaluate model drift, prompt regression, and agent task completion reliability across model upgrades

Requirements

- Core Experience: 5+ years of total software/machine learning engineering experience, with at least 2 years of focused, hands-on experience building and deploying Generative AI and LLM applications in production
- Programming CS Foundations: Advanced proficiency in Python (async programming, data structures, profiling, and package ecosystems)
- Generative AI NLP: Deep hands-on experience with modern LLM APIs, foundation model ecosystems, transformer architectures,



and classical NLP techniques
- Agent Orchestration Frameworks: Proven production track record using LangGraph, LangChain, or CrewAI to build multi-step, tool-augmented, or stateful agent systems
- RAG Vector Search: Strong working knowledge of indexing, embedding generation, chunking strategies, and vector databases (e.g., Pinecone, Milvus, Qdrant, OpenSearch, PGVector)
- Prompt Engineering: Demonstrated expertise in production-grade prompt templates, few-shot prompting, output parsing, schema enforcement (Pydantic), and safety guardrails

Desirable

- Backend System Design: Experience building microservices and RESTful/streaming endpoints using FastAPI or modern async frameworks
- LLMOps Tracing: Practical experience with evaluation and tracing suites like LangSmith, Langfuse, Arize Phoenix, or OpenTelemetry
- Cloud DevOps: Familiarity with AWS services (e.g., Bedrock, SageMaker, ECS, Lambda, S3), containerization with Docker, and orchestration with Kubernetes
- Software Quality CI/CD: Experience building automated CI/CD pipelines, static code analysis, and testing frameworks (pytest)
- Education: Bachelor s or Master s degree in Computer Science, Data Science, AI/ML, Mathematics, or a related quantitative field
- Opportunity to build cutting-edge agentic AI systems that drive measurable real-world outcomes
- Cooperative engineering culture focused on modern AI paradigms, autonomous agents, and production experimentation

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Senior Artificial Intelligence/Machine Learning Engineer (India)
🏢 Ciklum
📍 India