{"id":2834086,"url":"https://alion.io/job/treyansh-global-services-agentic-ai-engineer-pythonlanggraph","title":"Agentic AI Engineer - Python/LangGraph","company":{"id":4448079,"name":"Treyansh Global Services","domain":"treyansh.co.in","url":"https://alion.io/company/treyansh-global-services","size_band":null,"is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":18500,"max_usd":50000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":31},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon EKS","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"Chroma","optional":false},{"name":"CI/CD","optional":false},{"name":"Context Engineering","optional":false},{"name":"DeepEval","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"FAISS","optional":false},{"name":"FastAPI","optional":false},{"name":"Function Calling","optional":false},{"name":"Git","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Hybrid Search","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"Langfuse","optional":false},{"name":"LangGraph","optional":false},{"name":"LangSmith","optional":false},{"name":"LightGBM","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"OpenSearch","optional":false},{"name":"OpenTelemetry","optional":false},{"name":"Pandas","optional":false},{"name":"Pinecone","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Recommender Systems","optional":false},{"name":"Rest API","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Semantic Search","optional":false},{"name":"SQL","optional":false},{"name":"Structured Outputs","optional":false},{"name":"TensorFlow","optional":false},{"name":"XGBoost","optional":false},{"name":"Amazon Neptune","optional":true},{"name":"AWS Strands Agents","optional":true},{"name":"Fine-tuning","optional":true},{"name":"Google ADK","optional":true},{"name":"GraphRAG","optional":true},{"name":"Knowledge Distillation","optional":true},{"name":"Knowledge Graph","optional":true},{"name":"Least Privilege","optional":true},{"name":"LLM Guardrails","optional":true},{"name":"LoRA","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"Model Distillation","optional":true},{"name":"OpenAI Agents SDK","optional":true},{"name":"OWASP Top 10","optional":true},{"name":"PEFT","optional":true},{"name":"Prompt Caching","optional":true},{"name":"Quantization","optional":true},{"name":"Transformers","optional":true}],"status":"live","first_seen_at":"2026-10-19T04:06:17Z","employer_posted_date":null,"last_verified_at":"2026-10-19T04:06:17Z","board_verified":false,"closed_at":null,"days_open":3,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":3},"description":"Role : Agentic AI Engineer\n\nTeam : Agentic Automation\n\nLocation : Bangalore (Hybrid: Minimum 12 workdays WFO per month)\n\nExperience : 3 - 7 Years (Minimum 3 years relevant experience in AI/ML engineering)\n\nCore Skills : Python (FastAPI), LangGraph, LangChain, Production RAG, AWS (Bedrock), Docker/Kubernetes\n\nRole Overview :\n\nWe are looking for a highly skilled Senior AI & Machine Learning Engineer with hands-on experience in Python-based AI/ML engineering and strong expertise across Traditional Machine Learning, Generative AI, and Agentic AI. You will play a critical role in designing, developing, and deploying scalable AI solutions using machine learning models, large language models (LLMs), agentic workflows, cloud technologies, APIs, and modern data platforms.\n\nKey Responsibilities :\n\n- Design and orchestrate LLM-powered applications, production RAG solutions, and stateful agentic workflows using LangGraph and LangChain, including agent state, memory, and tool calling.\n\n- Design, develop, and deploy AI/ML solutions spanning predictive analytics, recommendation systems, forecasting, anomaly detection, and Generative AI use cases.\n\n- Build and operationalize machine learning models, driving the complete lifecycle from data preparation and feature engineering through deployment and monitoring.\n\n- Develop scalable APIs, backend services, and integration components using Python and FastAPI.\n\n- Architect and deploy cloud-native AI solutions on AWS, ensuring security, scalability, reliability, and cost efficiency.\n\n- Build intelligent retrieval and knowledge-grounding capabilities using vector databases, semantic search, and embedding models.\n\n- Implement MLOps practices including CI/CD, model versioning, experiment tracking, monitoring, and automated deployments.\n\n- Collaborate with architects, data engineers, product teams, and business stakeholders to translate business requirements into AI-driven solutions.\n\n- Optimize model performance, application reliability, latency, and operational efficiency in production environments.\n\n- Build evaluation and observability into every agentic release, using test datasets, automated evaluation, tracing, and cost and latency monitoring to prove quality before and after deployment.\n\n- Evaluate emerging AI, machine learning, and agentic framework technologies and drive adoption of relevant innovations.\n\nMust-Have Skills :\n\n- Advanced Python and FastAPI : Production-grade Python (async, typing, testing, packaging) and REST APIs and microservices built with FastAPI.\n\n- LangGraph / LangChain : Hands-on delivery of multi-step, stateful agentic workflows, including graph orchestration, conditional routing, and human-in-the-loop checkpoints.\n\n- Production RAG : RAG systems running in production, covering chunking, embeddings, hybrid search, re-ranking, context engineering, and retrieval-quality tuning.\n\n- Vector databases : Amazon OpenSearch Service, FAISS, Pinecone, Chroma, or equivalent, including index design, metadata filtering, and scaling.\n\n- Agent state and memory : Short-term and long-term memory design, checkpointing, state persistence, and session management for long-running agents.\n\n- Tool calling : Tool and function schemas, structured outputs, error handling, retries, and safe execution of tools with side effects.\n\n- Evaluation frameworks : RAGAS, DeepEval, LangSmith evaluations, or equivalent, with golden datasets, LLM-as-judge scoring, and regression testing in CI.\n\n- Observability : Tracing, logging, and metrics for LLM and agent pipelines using Langfuse, OpenTelemetry, or equivalent, with token, cost, and latency tracking.\n\n- Docker / Kubernetes : Containerizing and deploying AI services on Kubernetes (Amazon EKS preferred), with Git, CI/CD pipelines, and automated testing.\n\nCore Engineering Foundations :\n\n- Minimum 3 years of professional experience in AI/ML engineering, including hands-on experience developing and deploying production-grade AI/ML solutions.\n\n- Demonstrated experience delivering solutions across traditional ML and Generative AI or Agentic AI.\n\n- Experience developing Generative AI and LLM-powered solutions using Amazon Bedrock, and proficiency with AWS services and cloud-native architecture for AI/ML workloads.\n\n- Hands-on experience with machine learning libraries and frameworks such as Pandas, NumPy, scikit-learn, XGBoost, LightGBM, TensorFlow, or PyTorch.\n\n- Sound understanding of supervised and unsupervised learning, feature engineering, model evaluation, hyperparameter tuning, and model explainability.\n\n- Experience working with SQL and NoSQL databases, object storage, and data-processing pipelines.\n\n- Good understanding of scalable software design, API security, and production support.\n\nGood-to-Have Skills :\n\n- MCP and tool registries : Building or consuming Model Context Protocol (MCP) servers and managing governed tool registries for discovery, versioning, and access control.\n\n- AWS Strands Agents or Google ADK : Building agents on AWS Strands Agents SDK or Google Agent Development Kit (ADK).\n\n- Model optimization : Quantization, distillation, parameter-efficient fine-tuning (LoRA/PEFT), prompt caching, and optimized inference serving to cut latency and cost.\n\n- Secure AI engineering : Prompt-injection defence, guardrails (e.g., Amazon Bedrock Guardrails), PII handling, least-privilege tool access, and familiarity with the OWASP Top 10 for LLM Applications.\n\n- Knowledge graphs : Exposure to Graph RAG, entity relationship modeling, and graph databases such as Amazon Neptune or equivalent.\n\nQualifications :\n\n- B.Tech or Graduation in Computer Science Engineering or relevant field.\n\nWhat We're Looking For :\n\n- Excellent problem-solving and analytical skills.\n\n- Strong collaborative and communication skills.\n\n- Ability to independently design solutions and work effectively as part of a cross-functional team.\n\n- Ability to explain technical concepts to both technical and business stakeholders.\n\n- Passion for emerging AI technologies and innovation across machine learning and intelligent automation.\nSkills\nAgentic AI, Artificial Intelligence, Machine Learning, Python, LangChain, LangGraph, AWS, RAG, LLM, Generative 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