Hiring.Camp

Senior Agentic AI Developer

Cai

·

Yesterday

Location
India - Bengaluru
Workplace
Hybrid
Type
Full-time
Department
IT
Seniority
Senior
Source
Workday

Description

Senior Agentic AI Developer

Req number:

R8521

Employment type:

Full time

Worksite flexibility:

Hybrid

Who we are

CAI is a global services firm with over 9,000 associates worldwide and a yearly revenue of $1.3 billion+. We have over 40 years of excellence in uniting talent and technology to power the possible for our clients, colleagues, and communities. As a privately held company, we have the freedom and focus to do what is right—whatever it takes. Our tailor-made solutions create lasting results across the public and commercial sectors, and we are trailblazers in bringing neurodiversity to the enterprise.

Job Summary

We are looking for a motivated Senior Agentic AI Developer ready to take us to the next level! If you have experience in designing, developing, and deploying state-of-the-art agentic systems, automation solutions, and generative AI applications to enable autonomous decision-making and optimize business processes and are looking for your next career move, apply now.

Job Description

We are looking for a Senior Agentic AI Developer to design cutting-edge AI solutions and autonomous systems focused on agentic workflows and generative AI technologies. This position will be full-time and Hybrid Bengaluru.

What You’ll Do

  • Design, develop, and deploy multi-agent systems and agentic applications using frameworks like AutoGen, LangGraph, CrewAI, or similar

  • Build intelligent workflow orchestration systems that enable autonomous decision-making and task execution

  • Implement Agent-to-Agent (A2A) communication protocols and Model Context Protocol (MCP) for seamless agent collaboration

  • Develop automation solutions using OpenAPI standards for integration with enterprise systems

  • Create self-healing, adaptive workflows that optimize business processes autonomously

  • Use ML, deep learning, and Generative AI tools to design, evangelize, and implement state-of-the-art solutions

  • Define and implement best practices for building, testing, and deploying scalable AI solutions, with a focus on generative models and LLMs using proprietary or open-source models

  • Drive successful business outcomes by designing and building cloud-hosted Generative AI solutions

  • Work closely with internal teams to integrate RAG workflows, agent-based systems, and automation frameworks into applications

  • Design and implement architectural solutions for Information Retrieval using RAG, Vector DBs, and Knowledge Graphs

  • Work with public cloud (AWS) and on-premises infrastructure for deploying LLMs, agents, and orchestration systems

  • Evaluate, build, and fine-tune ML models and LLMs to solve complex business problems

  • Stay abreast of latest developments in agentic AI, autonomous systems, language models, and generative AI technologies

What You'll Need

Required:

  • 3-6 years of overall technical experience with 2 years of hands-on experience in Generative AI and LLM technologies

  • 1+ years of experience building agentic systems, workflow automation, or autonomous AI applications

  • Deep hands-on experience with agentic frameworks (AutoGen, LangGraph, CrewAI, Agency Swarm, or similar)

  • Strong knowledge of workflow orchestration tools and patterns (Temporal, Airflow, Prefect, or similar)

  • Expertise in OpenAPI standards, Agent-to-Agent (A2A) protocols, and Model Context Protocol (MCP)

  • Experience designing multi-agent architectures with memory, planning, and tool-use capabilities

  • Knowledge of agent evaluation, testing frameworks, and observability patterns

  • Proven track record of deploying and optimizing LLM models for inference in production environments

  • Extensive experience with LLM orchestration frameworks (LangChain, LlamaIndex required)

  • Hands-on experience with Amazon Bedrock, SageMaker JumpStart, and other cloud-based LLM platforms

  • Expertise in RAG architectures, Fine-tuning techniques, and Prompt Engineering

  • Deep understanding of Vector Databases (Pinecone, Weaviate, Milvus, ChromaDB) and Knowledge Graphs

  • Expert in NLP techniques and deep learning libraries (Transformer models, LSTM, BiLSTM, CNN, BERT, GPT, T5)

  • Proficiency with ML frameworks: TensorFlow, PyTorch, Hugging Face Transformers, scikit-learn

  • Strong programming skills in Python (required), plus JavaScript/TypeScript or Node.js

  • Deep understanding of data structures, algorithms, and system design patterns

  • Hands-on experience in MLOps/LLMOps including data pipelines, model training/refinement, validation, drift management, and serving

  • Experience with containerization (Docker, Kubernetes) and CI/CD pipelines for ML systems

  • Knowledge of monitoring, logging, and observability tools for production AI systems

Preferred:

  • Experience with function calling, tool use, and external API integration in agent systems

  • Knowledge of reinforcement learning and agent training methodologies

  • Familiarity with semantic reasoning, planning algorithms (ReAct, Chain-of-Thought, Tree-of-Thoughts)

  • Experience with graph databases (Neo4j, Neptune) and ontology design

  • Contributions to open-source AI/ML projects

  • Publications or patents in AI/ML domain

Physical Demands

  • Ability to safely and successfully perform the essential job functions

  • Sedentary work that involves sitting or remaining stationary most of the time with occasional need to move around the office to attend meetings, etc

  • Ability to conduct repetitive tasks on a computer, utilizing a mouse, keyboard, and monitor

Reasonable accommodation statement

If you require a reasonable accommodation in completing this application, interviewing, completing any pre-employment testing, or otherwise participating in the employment selection process, please direct your inquiries to [email protected] or (888) 824 – 8111.

Skills

PythonJavaScriptTypeScriptNode.jsAWSDockerKubernetesCI/CDDeep LearningNLPTensorFlowPyTorchScikit-learnAirflow

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