At EFS Facilities Services, we are looking for an innovative and technically strong Data & AI Engineer – Agentic AI & Predictive Intelligence to design, develop, and deploy enterprise-grade AI solutions that leverage Large Language Models (LLMs), Agentic AI, Retrieval-Augmented Generation (RAG), and Predictive Machine Learning.
Role Overview
The AI/ML Engineer will be responsible for designing, developing, deploying, and optimizing production-grade AI solutions that combine LLMs, Agentic AI, RAG architectures, and predictive analytics. The role will focus on developing scalable AI applications, implementing robust evaluation frameworks, optimizing model performance, and collaborating with cross-functional teams to transform complex business challenges into intelligent, production-ready solutions.
Key Responsibilities
Agentic AI Development
- Design and develop multi-agent AI systems using planner-executor architectures, tool orchestration, and autonomous reasoning frameworks.
- Architect agent memory capabilities, including short-term memory, long-term memory, and knowledge graph integration.
- Develop structured tool schemas and function-calling architectures to improve reliability and consistency of AI outputs.
- Implement guardrails, fallback mechanisms, and human-in-the-loop workflows for critical enterprise processes.
- Evaluate and optimize agent performance based on task completion, reasoning accuracy, reliability, and failure analysis.
Retrieval-Augmented Generation (RAG)
- Design and implement end-to-end RAG pipelines covering data ingestion, document chunking, embeddings, retrieval, and response generation.
- Develop advanced retrieval strategies including multi-query retrieval, query routing, HyDE, Graph RAG, and hybrid search techniques.
- Configure, optimize, and manage vector databases such as Pinecone, Weaviate, and pgvector for scalable enterprise applications.
- Continuously improve retrieval quality through precision, recall, relevance, and answer faithfulness metrics.
- Integrate structured and unstructured enterprise data into unified knowledge retrieval platforms.
Model Serving & Optimization
- Deploy and optimize Large Language Models using inference frameworks such as vLLM and similar serving platforms.
- Improve model efficiency through batching, caching, quantization, inference optimization, and latency tuning.
- Develop advanced prompt engineering strategies to improve response quality and consistency.
- Implement fine-tuning methodologies including LoRA, PEFT, and Supervised Fine-Tuning (SFT).
- Evaluate and recommend the optimal balance between prompt engineering, RAG, and fine-tuning based on business requirements.
Evaluation, AI Safety & Governance
- Develop comprehensive evaluation frameworks covering AI quality, latency, safety, reliability, and operational cost.
- Identify and mitigate AI risks including hallucinations, prompt injection attacks, retrieval failures, and model bias.
- Build reusable benchmarking and evaluation frameworks for continuous model assessment.
- Ensure compliance with enterprise AI governance standards, security policies, and ethical AI practices.
- Monitor production AI systems and implement continuous performance improvements.
Predictive Analytics & Machine Learning
- Design, develop, and deploy machine learning models for classification, regression, ranking, forecasting, and predictive analytics.
- Perform feature engineering, feature selection, dimensionality reduction, and model optimization based on business objectives.
- Manage the complete machine learning lifecycle including experimentation, validation, deployment, monitoring, and continuous improvement.
- Integrate predictive ML models with LLM-powered systems to enable intelligent decision-making.
- Present analytical insights, model performance metrics, confidence scores, and explainability to business stakeholders.
Collaboration & Engineering Excellence
- Collaborate closely with Product, Engineering, Data, and Operations teams to develop AI-powered enterprise solutions.
- Translate complex business requirements into scalable, production-ready AI architectures.
- Contribute to reusable AI frameworks, internal libraries, engineering standards, and best practices.
- Promote continuous innovation, knowledge sharing, and technical excellence within the AI engineering function.
- Drive continuous optimization based on production feedback, system monitoring, and evolving business needs.
What We're Looking For
- 3–5 years of experience in Software Engineering, AI Engineering, Machine Learning, or Data Engineering.
- Minimum 3+ years of hands-on experience building and deploying LLM-based applications in production environments.
- Minimum 2+ years of experience developing and deploying predictive machine learning models.
- Strong expertise working with foundation models such as GPT, Claude, Falcon, Llama, or similar LLMs.
- Hands-on experience with LangChain, LangGraph, LlamaIndex, CrewAI, or equivalent GenAI orchestration frameworks.
- Experience building enterprise RAG applications and working with vector databases such as Pinecone, Weaviate, or pgvector.
- Strong programming skills in Python and SQL with working knowledge of TypeScript.
- Experience with Scikit-learn, PyTorch, XGBoost, and other machine learning frameworks.
- Experience with model serving platforms including vLLM, TGI, or similar inference optimization technologies.
- Strong understanding of MLOps, feature engineering, model evaluation, AI architecture, and production deployment.
- Excellent analytical, problem-solving, debugging, and stakeholder communication skills.
Educational Qualifications
- Bachelor's or Master's Degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field.
- Professional certifications in AI/ML, Cloud Computing, Data Engineering, or MLOps will be an added advantage.
Why Join EFS Facilities Services
- Be part of a leading global Facilities Management organization driving enterprise AI transformation.
- Work on cutting-edge technologies including Agentic AI, LLMs, RAG, and Predictive Analytics.
- Build scalable AI platforms with real-world impact across multiple business functions.
- Collaborate with multidisciplinary teams in a dynamic, innovation-driven environment.
- Enjoy continuous learning opportunities, career growth, and exposure to enterprise-scale AI initiatives.
Location
Bangalore,India
