Associate Process Manager at eClerx Career Site

, maharashtra, India -

Full Time

Start Date

Immediate

Expiry Date

14 Oct, 26

Salary

0.0

Posted On

16 Jul, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, LLM Engineering, Agent Orchestration, RAG Implementation, FastAPI, Prompt Engineering, Vector Databases, Docker, CI/CD, LangChain, LangGraph, LlamaIndex, API Development, Evaluation Pipelines, Cloud Deployment, Git

Industry

IT Services and IT Consulting

Description

Role summary Build the GenAI and agentic AI solution. This role combines LLM engineering, agent orchestration, RAG implementation, backend APIs, evaluation, deployment, and production support. Hire three people under this common JD, but ensure the team collectively covers agent workflows, RAG / knowledge systems, and platform / AgentOps. Key responsibilities • Build agentic workflows: Implement agent behavior, prompts, tool/function calling, structured outputs, state management, memory, handoffs, and human approval checkpoints. • Implement RAG systems: Build document ingestion, parsing, chunking, embeddings, vector indexes, hybrid search, re-ranking, metadata filtering, and citation generation. • Develop backend services: Create production APIs and services using Python, FastAPI or similar frameworks, integrated with LLM providers and enterprise systems. • Integrate enterprise tools: Connect agents with business APIs, databases, search systems, workflow tools, CRM, ERP, ticketing systems, and collaboration platforms. • Create evaluation pipelines: Build test datasets, prompt regression tests, retrieval quality checks, LLM-as-judge evaluations, and human feedback loops. • Support deployment and operations: Containerize services, participate in CI/CD, configure monitoring, trace agent runs, track cost and latency, and troubleshoot production issues. • Improve quality and safety: Implement guardrails, input/output validation, prompt injection defenses, access controls, audit logging, and fallback handling. Required skills and experience • Strong Python engineering skills with production-quality coding practices, Git, testing, packaging, and code reviews. • Hands-on experience with LLM APIs and frameworks such as OpenAI, Azure OpenAI, Anthropic, Gemini, Bedrock, LangGraph, LangChain, LlamaIndex, Semantic Kernel, CrewAI, or similar. • Practical understanding of prompt engineering, structured outputs, tool/function calling, agent orchestration, memory/state, and multi-step reasoning workflows. • Experience with RAG, embeddings, vector databases, document ingestion, search relevance, metadata filters, and retrieval evaluation. • Backend development experience with FastAPI, Flask, Django, or similar API frameworks. • Experience with Docker, CI/CD, cloud deployment, secrets management, logging, monitoring, and environment configuration. • Ability to debug agent failures, hallucinations, retrieval misses, malformed tool calls, latency spikes, and token cost issues. Preferred / good-to-have skills • Experience with Azure AI Search, Pinecone, Weaviate, Milvus, pgvector, Elasticsearch, OpenSearch, or similar search/vector platforms. • Experience with observability and evaluation tools such as LangSmith, MLflow, Arize Phoenix, OpenTelemetry, Datadog, Azure Monitor, or CloudWatch. • Experience building agents that interact with real business systems under permissions, audit, and human-approval constraints. • Experience with Kubernetes, Terraform, API gateways, authentication, authorization, and enterprise security patterns. • Basic frontend experience for rapid demos using React, Streamlit, Next.js, or similar tools is useful but not mandatory. Expected outputs and success measures • Working agent workflows that perform useful tasks with reliable tool calls and clear fallback handling. • Production-ready RAG pipeline with measurable retrieval quality, citations, and access-control awareness. • Backend APIs, deployment artifacts, CI/CD pipeline, monitoring dashboards, and evaluation reports. • Reduced hallucination rate, improved retrieval quality, controlled token cost, acceptable latency, and stable production behavior. About eClerx eClerx is a global leader in productized services, bringing together people, technology and domain expertise to amplify business results. Our mission is to set the benchmark for client service and success in our industry. Our vision is to be the innovation partner of choice for technology, data analytics and process management services. Since our inception in 2000, we've partnered with top companies across various industries, including financial services, telecommunications, retail, and high-tech. Our innovative solutions and domain expertise help businesses optimize operations, improve efficiency, and drive growth. With over 18,000 employees worldwide, eClerx is dedicated to delivering excellence through smart automation and data-driven insights. At eClerx, we believe in nurturing talent and providing hands-on experience. About eClerx Digital eClerx Digital is the trusted partner of choice to many of the world’s largest global brands for creative production, e-commerce/web operations, and analytics and insights services. We help clients maximize the results of their digital activities across the customer journey. Global industry leaders partner with us to cost-effectively scale their digital activities by leveraging our highly skilled blended resource model. To know more about us, visit https://eclerx.com [https://eclerx.com] eClerx is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability or protected veteran status, or any other legally protected basis, per applicable law. A Trusted Innovation Partner, Delivering Business Results | eClerx eClerx is a global leader in productized services, enhancing business outcomes through technology, Artificial Intelligence, and deep domain expertise.

Responsibilities

Build and deploy GenAI and agentic AI solutions, focusing on agent workflows, RAG systems, and backend API development. The role involves creating evaluation pipelines and ensuring production stability through monitoring and guardrail implementation.