We are looking for an experienced AI/LLM Engineer to design, build, and maintain intelligent applications powered by Large Language Models (LLMs), embeddings, similarity search, vector databases, and multi-agent architectures.

The ideal candidate will build real-time AI systems such as chatbots, semantic search engines, recommendation systems, document intelligence platforms, MCP servers, and autonomous multi-agent workflows capable of tool usage and inter-agent communication.

You will own the end-to-end lifecycle of AI pipelines including data ingestion, embedding generation, vector storage, retrieval, LLM response orchestration, tool invocation, agent communication, and automated decision workflows.

- Experience: 5+ Years overall, with over 1 year experience in building Agentic AI
- Location: Bangalore
- Employment Type: Full-Time

Key Responsibilities ● Design and implement embedding pipelines for text, documents, images, and structured data.

● Build and optimize semantic search and similarity search systems using vector databases. ● Integrate and manage vector databases such as: ○ Pinecone, Weaviate, Milvus, FAISS, Chroma, OpenSearch Vector Engine, etc.

● Develop LLM-powered applications for:

○ Chatbots

○ Q&A; systems

○ Recommendation engines

○ AI agents and automation workflows

● Implement RAG (Retrieval Augmented Generation) pipelines with hybrid retrieval and reranking.

● Design and develop multi-agent architectures (planner-executor, supervisor-worker, tool-using agents)

. ● Build and deploy MCP (Model Context Protocol) servers to expose tools, memory, and external systems to LLM agents.

● Develop structured agentic workflows using frameworks like LangGraph, Strands, or similar orchestration engines.

● Implement multi-agent communication using A2A (Agent-to-Agent) protocols for collaborative reasoning and task execution.

● Design tool-calling pipelines and function-calling integrations.

● Fine-tune prompt strategies, memory handling, and system prompts for optimal LLM performance.

● Integrate LLM providers such as: ○ OpenAI, Azure OpenAI, Anthropic, Google Gemini, Meta LLaMA, Mistral, etc.





● Build APIs and microservices for AI systems using: ○ Python / Java / Node.js / Spring Boot / FastAPI

● Implement similarity scoring, ranking, filtering, and metadata-based retrieval. WORKFALL ● Monitor, optimize, and scale vector search performance.

● Optimize LLM cost, latency, caching, and response validation strategies.

● Implement AI safety mechanisms, hallucination reduction, guardrails, and evaluation pipelines.

● Work closely with product, frontend, and data teams.

● Deploy AI workloads on AWS, Azure, GCP, or OCI.

● Maintain CI/CD pipelines for AI services. Required Skills & Qualifications Mandatory Core AI, LLM & Agentic Skills

● Strong understanding of:

○ Embeddings

○ Vector similarity search

○ Cosine similarity, dot product, ANN indexing

○ RAG architectures

● Hands-on experience with:

○ LangChain / LlamaIndex / Semantic Kernel / Spring AI

● Experience building multi-agent systems and agent orchestration pipelines

● Experience building MCP servers for tool and context exposure

● Experience with LangGraph / Strands or similar agent workflow orchestration tools

● Experience implementing A2A (Agent-to-Agent) communication patterns

● Proficient in prompt engineering, memory management, and LLM orchestration

● Experience with at least one Vector Database Programming & Backend

● Strong proficiency in Python / Java / JavaScript / TypeScript

● API development using FastAPI, Flask, Spring Boot, or Node.js

● Strong understanding of REST APIs, async processing, event-driven architectures

● Experience building microservices for AI agents. Data & Storage

● Experience with:

○ PostgreSQL, MySQL, MongoDB

○ Object storage (S3, OCI, Azure Blob)

● Data preprocessing, chunking strategies, tokenization optimization





● Knowledge of metadata filtering and hybrid search Cloud & DevOps

(Positive to Have)

● Docker & Kubernetes

● CI/CD pipelines (Jenkins, GitHub Actions, GitLab, Bitbucket)

● Monitoring with Prometheus, Grafana, OpenTelemetry WORKFALL

● Experience deploying scalable AI inference pipelines

Good to Have (Preferred Skills)

● Deep experience with Agentic AI frameworks

● Knowledge of Tool Calling / Function Calling

● Experience with workflow engines and orchestration graphs

● Experience with Speech-to-Text, Vision models

● Fine-tuning, LoRA, PEFT experience

● Knowledge of AI security, governance & data privacy

● Experience building autonomous AI systems with memory + tools

● Experience designing distributed agent architectures

Use Cases You Will Work On

● AI chatbots for customer support

● Semantic document search

● Knowledge-base Q&A; systems

● Multi-agent workflow automation

● Intelligent AI copilots

● Automated ticket triaging

● AI assistants for developers and operations

● Collaborative agent systems using A2A protocols

● MCP-based tool-integrated AI systems

Pay: ₹1,500,000.00 - ₹1,800,000.00 per year

Application Question(s)

- What is your current notice period?
- How much annual salary are you expecting?
- What is your current annual salary?
- The CTC for this role is in the range of 15–18 LPA. Are you comfortable with this?
- This interview will be conducted face‑to‑face at our Whitefield Bangalore office. Will you be able to attend onsite?
- Do you have hands‑on experience implementing Retrieval‑Augmented Generation (RAG) pipelines using at least one vector database (e.g., Pinecone, FAISS, Weaviate)?
- Have you built or deployed multi‑agent architectures (e.g., planner‑executor, supervisor‑worker) with inter‑agent communication using frameworks like LangGraph or Strands?
- Are you proficient in Python or Java, with experience building APIs/microservices for AI systems using FastAPI, Flask, Spring Boot, or Node.js?

Location:
- Bengaluru, Karnataka (Required)

Work Location: In person

📌 AI Engineer (Bengaluru)
🏢 Workfall India
📍 Bengaluru