Senior AI Engineer – LLMs, RAG, Agents

We are seeking a Senior AI Engineer to join our team and take a lead role in designing, scaling, and operationalizing next-generation AI systems. In this role, you will architect and deliver production-grade solutions leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, and autonomous multi-agent workflows. You will define the infrastructure, tooling, and engineering strategy that power our most critical AI initiatives while collaborating with Data Scientists, ML Engineers, Data Engineers, and Product teams.Key Responsibilities

As a Senior AI Engineer, you will:LLM + Agentic Systems Engineering

Architect, design, and implement LLM-powered applications, including conversational AI, RAG pipelines, copilots, and generative decision-support tools
Build and optimize autonomous agents and multi-agent systems for complex workflows and internal/client AI automation
Integrate model orchestration frameworks (LangChain, LangGraph, Semantic Kernel, etc.) for scalable AI agent behavior

AI Infrastructure & Operations (AIOps)

Establish and maintain AIOps practices for monitoring, drift detection, evaluation, observability, and continuous improvement of AI models
Productionize and scale AI workloads on AWS using Lambda, ECS/EKS, Step Functions, Batch, and S3-based data architectures

ML Pipelines & Backend Engineering

Build and maintain end-to-end ML workflows, ETL/ELT pipelines, vector indexing flows, and embedding generation systems
Develop high-performance backend services in Python integrating SQL, NoSQL, and vector database engines (Pinecone, FAISS, Chroma, Weaviate)
Design real-time and event-driven architectures using SNS/SQS, Kafka/MSK, Kinesis, or similar platforms for AI integration

Cross-functional Leadership

Work closely with Product, Data Science, and Engineering teams to define requirements and drive technical execution
Lead technical reviews, mentor engineers, and contribute to engineering best practices, reliability, and scalability standards
Stay up to date with advancements in LLM research, agentic frameworks, model fine-tuning, evaluation, and AI tooling

QualificationsRequired

Bachelor’s or Master’s in Computer Science, Engineering, or a related STEM field
6+ years of experience in AI/ML engineering with a strong focus on LLMs, RAG systems, and agentic architectures
Proven expertise building production AI applications with OpenAI, Anthropic, Azure OpenAI, or open-source LLMs
Strong proficiency in Python, SQL, backend API development, and distributed system design
Hands-on experience with AWS, Docker, Kubernetes, CI/CD pipelines, and IaC (Terraform/CloudFormation)
Deep knowledge of vector databases, semantic search, embeddings, and similarity search workflows
Experience with real-time, event-driven, or streaming architectures
Strong collaboration, communication, and leadership skills

Preferred (Nice-to-have)

Experience fine-tuning or distilling LLMs
Knowledge of GPU workloads, Triton inference servers, or model optimization techniques
Prior experience building evaluation frameworks (Ragas, DeepEval, PromEval, custom metrics)

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Here is a clean, polished, professional Job Description you can directly send to vendors, recruiters, or post on LinkedIn.Job Title: Senior AI EngineerLocat</st

rong>ion: Hybrid/Remote

Employment Type: Contract / Full-Time

Experience: 6+ Years

Start Date: ImmediateJob Summary

We are seeking a Senior AI Engineer to design, build, and scale advanced AI systems for our enterprise and client-facing platforms. The ideal candidate has strong hands-on experience with LLMs, RAG architectures, AI agents, and production-grade ML systems. You will play a key role in architecting our AI ecosystem, building scalable services, and driving technical excellence across multiple AI initiatives.

This role requires deep technical expertise in Python, AWS, ML pipelines, and distributed systems — along with the ability to collaborate with cross-functional teams and lead end-to-end AI solution delivery.Key ResponsibilitiesAI/LLM Engineering

Architect, design, and build LLM-powered applications including chatbots, copilots, and AI-driven decision-support tools.Implement Retrieval-Augmented Generation (RAG) pipelines with vector databases, embeddings, and semantic search.Develop autonomous AI agents and multi-agent systems using frameworks such as LangChain, LangGraph, or Semantic Kernel.

AI Infrastructure & AIOps

Build production-ready AI systems with robust monitoring, evaluation, drift detection, and continuous improvement loops.Deploy and scale AI workloads on AWS using Lambda, ECS/EKS, EC2, Step Functions, and cloud-native tooling.Maintain model lifecycle management, versioning, deployment automation, and CI/CD practices.

ML Pipelines & Backend Development

Build and optimize data ingestion, ETL/ELT workflows, and ML pipelines using Python and SQL.Integrate vector databases such as Pinecone, FAISS, Chroma, or Weaviate.Build scalable backend APIs and microservices to support AI features and real-time inference.

Cross-functional Collaboration

Partner with Data Scientists, ML Engineers, Data Engineers, and Product Managers to define technical requirements.Lead code reviews, mentorship, and contribute to engineering best practices.Influence architecture roadmaps and ensure high-quality, high-impact delivery.

Required Qualifications

Bachelor’s or Master’s in Computer Science, Engineering, or a related technical field.6+ years of AI/ML engineering experience with production systems.Strong expertise with LLMs, prompt engineering, RAG systems, and agentic workflows.Advanced proficiency in Python, SQL, software engineering, and system design.Hands-on experience with AWS, Docker, Kubernetes, CI/CD, and IaC tools (Terraform/CloudFormation).Experience working with vector databases and semantic search.Understanding of real-time, event-driven architectures (Kafka, Kinesis, SQS/SNS).Strong Git, Agile delivery, communication, and leadership skills.

Preferred Qualifications

Experience with fine-tuning, quantization, or optimizing LLMs.Experience with GPU workloads, Triton Inference Server, or distributed training.Knowledge of AIOps frameworks, evaluation tools, and performance benchmarking.

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