Immediate openings For AI/ML Engineer @Knack RCM, Hyderabad
Posted Updated
Summary
AI/ML Engineer (10 openings, 2-10 yrs experience) at EqualizeRCM's Knack RCM in Hyderabad, working on-site to build Agentic AI workflows with LangChain/LangGraph and AWS Bedrock, RAG/GraphRAG pipelines, LLM evaluation, and traditional ML models for healthcare revenue-cycle problems.
Immediate joining for AI/ML Engineer @Knack RCM, Hyderabad
Designation: AI/ML Engineer
Experience: 2 to 10 years
Work Location: Hyderabad
No. of positions: 10
Key Responsibilities
- Design, build, and deploy Agentic AI workflows using LangChain and LangGraph or equivalent.
- Build and manage agents using AWS Bedrock AgentCore (or similar AWS-native agent infrastructure), including deployment, memory, identity, and tool integration on AWS
- Architect and optimize RAG and GraphRAG pipelines, chunking strategies, embedding selection, knowledge graph construction, vector/graph database design, and retrieval evaluation
- Build robust evaluation frameworks for LLM outputs (accuracy, hallucination rate, groundedness, latency, cost)
- Design multi-agent systems: planning, tool use, memory, and orchestration across agents
- Build, train, and validate traditional ML models (regression, classification, clustering, tree-based models, time-series forecasting) for structured/tabular data problems
- Perform feature engineering, data preprocessing, and exploratory data analysis for ML pipelines
- Evaluate and select appropriate ML approaches (classic ML vs. deep learning vs. LLM-based) based on problem fit, data availability, and cost/latency constraints
- Collaborate with product, clinical/domain, and engineering teams to translate business problems into AI-driven solutions
- Optimize for latency, cost, and reliability in production ML/LLM systems (caching, batching, model routing)
Required Qualifications
- 2+ years of professional experience in AI/ML engineering or software engineering
- Solid foundation in traditional ML and/or LLMs: algorithms (regression, classification, ensemble methods, clustering), model evaluation (precision/recall, ROC-AUC, cross-validation), and feature engineering, hands-on experience with ML libraries such as scikit-learn, pandas, NumPy, strong hands-on experience with traditional ML, LLMs, Agentic AI
- Comfort working in a fast-paced environment
Interested candidates please share your resume to: tateam@equalizercm.com or send to WhatsApp- 8220572707
Regards,
TATeam