AI Engineer

Job Description Job Description

We are unable to sponsor or take over sponsorship of an employment visa at this time.

Job Title: Senior Gen AI / Agentic AI Engineer Role Overview We are seeking a highly skilled

Senior Gen AI / Agentic AI Engineer

to design, build, and deploy enterprise-grade Generative AI and Agentic AI platforms. The role requires strong hands-on experience across

LLMs, RAG, Graph RAG, multi-agent orchestration, vector databases, MCP setup, full-stack application development, cloud-native deployments, observability, and data engineering pipelines . The ideal candidate should be capable of building scalable AI platforms from end to end, including data ingestion, embedding pipelines, retrieval systems, agent workflows, model serving, API layers, UI/UX integration, monitoring, security, and production deployment across multi-cloud environments. Key Responsibilities Design and develop

enterprise Gen AI and Agentic AI applications

using LLMs, RAG, Graph RAG, multi-agent workflows, and tool-augmented reasoning. Build scalable

RAG pipelines

including document ingestion, chunking, embedding generation, metadata enrichment, hybrid search, reranking, retrieval optimization, and response grounding. Implement

Graph RAG

solutions by integrating knowledge graphs, entity extraction, relationship mapping, graph traversal, and contextual retrieval. Develop

multi-agent systems

using frameworks such as

LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, LlamaIndex , and custom orchestration patterns. Set up and integrate

MCP servers and clients

to enable tool connectivity, enterprise system integration, agent-to-tool communication, and reusable AI workflows. Build and manage

vector database solutions

using Pinecone, Weaviate, Milvus, FAISS, Chroma, OpenSearch Vector Engine, Azure AI Search, Vertex AI Vector Search, or pgvector. Deploy and optimize

LLM / VLLM inference stacks

using vLLM, Hugging Face Transformers, TensorRT-LLM, TGI, Ollama, llama.cpp, Ray Serve, or Triton Inference Server. Integrate commercial and open-source LLMs such as

GPT, Claude, Gemini, Llama, Mistral, Mixtral, Falcon, Cohere, DeepSeek , and domain-specific fine-tuned models. Develop secure and scalable backend services using

Python, FastAPI, Flask, Node.js, Java/Spring Boot , or similar API frameworks. Build full-stack applications with frontend technologies such as

React, Angular, Next.js, TypeScript, JavaScript, HTML, CSS , and integrate AI workflows into user-facing interfaces. Create intuitive

UI/UX experiences

for AI chatbots, agent workbenches, document intelligence platforms, prompt playgrounds, feedback loops, approval workflows, and human-in-the-loop systems. Implement

data engineering pipelines

using Spark, PySpark, Databricks, Airflow, Kafka, Snowflake, BigQuery, Redshift, SQL, NoSQL, and cloud-native data services. Build ingestion pipelines for structured, semi-structured, and unstructured data including PDFs, Word documents, emails, images, logs, databases, APIs, and enterprise repositories. Deploy AI workloads on

AWS, Azure, and GCP , using services such as Bedrock, SageMaker, Azure OpenAI, Azure AI Search, Azure ML, Vertex AI, BigQuery, GKE, AKS, EKS, Lambda, and Cloud Functions. Implement cloud-native architecture using

Docker, Kubernetes, Helm, Terraform, CI/CD, GitHub Actions, GitLab, Jenkins , and infrastructure-as-code practices. Establish strong

monitoring and observability

for Gen AI applications, including prompt/response tracing, token usage, latency, hallucination tracking, retrieval quality, cost monitoring, model drift, and agent execution traces. Use tools such as

LangSmith, Arize Phoenix, W&B Weave, MLflow, Evidently AI, Prometheus, Grafana, OpenTelemetry, Splunk, Datadog, ELK , and cloud-native logging platforms. Implement

LLMOps / MLOps

practices including model registry, prompt versioning, evaluation pipelines, A/B testing, guardrails, feedback capture, safety checks, and automated deployment. Apply security and governance controls including

PII detection, data masking, access control, RBAC, IAM, encryption, audit logging, policy enforcement, prompt injection prevention, and responsible AI guardrails . Collaborate with product owners, architects, data scientists, engineers, UX teams, security teams, and business stakeholders to deliver production-grade AI solutions. Optimize Gen AI applications for

accuracy, latency, scalability, reliability, cost, and user experience .

Must Have Skills Strong hands-on experience in

Generative AI, LLMs, Agentic AI, RAG, Graph RAG, and prompt engineering . Experience building

multi-agent AI systems

using LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar frameworks. Strong understanding of

LLM orchestration , tool calling, function calling, agent memory, planning, reasoning, task decomposition, and workflow automation. Hands-on experience with

RAG architecture , including chunking strategies, embeddings, vector search, hybrid search, reranking, metadata filtering, and evaluation. Experience with

vector databases

such as Pinecone, Weaviate, Milvus, FAISS, Chroma, Azure AI Search, OpenSearch, Vertex AI Vector Search, or pgvector. Knowledge of

Graph RAG / knowledge graph solutions

using Neo4j, Neptune, TigerGraph, RDF, SPARQL, Cypher, or graph-based retrieval patterns. Strong backend development experience with

Python, FastAPI, Flask, Node.js, Java, REST APIs, GraphQL, microservices , and event-driven systems. Full-stack development experience with

React, Angular, Next.js, TypeScript, JavaScript, HTML, CSS , and API integration. Experience with

model serving and inference optimization

using vLLM, Hugging Face, TGI, Triton, TensorRT-LLM, Ray Serve, or similar platforms. Strong cloud experience across

AWS, Azure, and/or GCP . Hands-on experience with

Docker, Kubernetes, Helm, Terraform, CI/CD pipelines , and cloud-native deployments. Strong data engineering skills using

SQL, Python, Spark/PySpark, Databricks, Airflow, Kafka , and cloud data platforms. Experience implementing

observability, monitoring, logging, tracing, and evaluation

for AI/ML/LLM applications. Strong understanding of

LLMOps/MLOps , model lifecycle management, prompt lifecycle, evaluation metrics, and production support. Experience with

security, governance, responsible AI, guardrails, prompt injection protection, and enterprise compliance controls . Nice to Have Skills Hands-on experience setting up

MCP server/client architecture

for enterprise agentic AI platforms. Experience with

A2A / agent-to-agent communication , multi-agent collaboration, and tool registry patterns. Experience with

fine-tuning, LoRA, QLoRA, PEFT, RLHF, RLAIF , or domain-specific model adaptation. Experience with

document intelligence

platforms, OCR, extraction pipelines, and intelligent search. Experience with

Databricks Mosaic AI, Azure AI Foundry, AWS Bedrock Agents, Google Vertex AI Agent Builder , or similar enterprise AI platforms. Experience with

semantic caching , token optimization, prompt compression, and cost optimization. Familiarity with

Neo4j, AWS Neptune, OpenSearch, Elasticsearch, Redis, MongoDB, PostgreSQL, Snowflake, BigQuery . Experience in regulated industries such as

banking, financial services, healthcare, or insurance .

Kaleidoscope, an Infosys Company, is an equal opportunity employer, and all qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, spouse of protected veteran, or disability.