AI Engineer

Job description:

We are looking for an AI Engineer with strong AI/ML and GenAI systems expertise to build and produce next-generation AI systems. This role is for someone who can go beyond using frameworks someone who understands how models and AI systems work internally, can design the architecture around them, evaluate them rigorously, and take a prototype all the way to a scalable, production-grade system.

The candidate will lead the following workstreams:

AI/ML & Multimodal AI

Strong first-principles understanding of Machine Learning, Deep Learning and Computer Vision

Deep understanding of LLMs and Vision-Language Models (VLMs) tokenization/encoding, vision & language representations, multimodal alignment and how semantic information is fused

SFT for LLMs/VLMs, PEFT/LoRA and fine-tuning strategies

Model optimization: quantization (AWQ, INT4, HQQ), distillation, batching and inference optimization

vLLM and high-performance model serving

Reward modelling, custom/verifiable rewards, DPO, RLHF and GRPO

Hands-on experience with PyTorch, Hugging Face Transformers and TRL

RAG, Retrieval & AI Agents

Build advanced RAG / GraphRAG / VisionRAG systems

Retrieval & ranking: BM25, semantic retrieval, hybrid search, vector databases

Understand and implement RRF, MRR, Recall, Precision, F1, RAGAS and other evaluation approaches

Query optimization using HyDE, query expansion and rewriting

Agentic systems using LangGraph, LangChain, CrewAI, Agno or equivalent

Understanding of MCP, its communication/transport mechanisms, tool calling and context engineering

Structured output generation, schema validation and reliable tool execution

Evaluation & ML Engineering

Build evaluation harnesses, automated testing and benchmarking frameworks

Design golden datasets, taxonomies and evaluation datasets

Perform model benchmarking, error analysis and root-cause analysis of model failures

Design data preprocessing, transformation and ML pipelines

Understand model quality vs. latency vs. memory vs. cost trade-offs

Experience with distributed ML systems using Ray or equivalent.

Production AI Systems & Architecture

Design end-to-end AI/ML system architectures

Taking rapid prototypes robust production systems

Build Python/FastAPI microservices and REST APIs

Docker/containerization, webhooks and SSE

CI/CD, Git/GitHub and production engineering practices

Design scalable pipelines involving queues, asynchronous processing and distributed workloads

Hands-on exposure to multi-GPU training/inference is highly desirable

An ideal candidate will have the following Background and Skills:

Background:

Undergraduate Degree in any quantitative discipline such as engineering or science from a Top-Tier institution. MBA is a plus.

Minimum 3 years of relevant experience in GEN AI.

Strong hands-on experience in AI/ML infrastructure, cloud platforms, and production-grade ML systems.

Prior experience working with AWS or GCP cloud services, particularly services such as AWS Bedrock, SageMaker, EC2, S3, SQS, Lambda, ECR, EKS, CloudWatch, or GCP Vertex AI.

Experience with GPU infrastructure, CUDA, multi-GPU environments, and distributed training is required.

Hands-on experience with containerized and Kubernetes-based environments, including Docker and Kubernetes.

Experience working with LLMs, model training, fine-tuning, inference, and deployment using modern ML frameworks and tools.

Experience building and deploying scalable APIs and ML services in production environments.

Strong understanding of Linux, networking/API fundamentals, REST APIs, and CI/CD practices.

Skills:

Strong hands-on experience with Python, PyTorch, Hugging Face Transformers, TRL, FastAPI, Docker, vLLM, and Ray.

Very good understanding of SQL/PostgreSQL, Vector Databases, and Redis

Hands-on experience with Kubernetes and distributed computing environments.

Strong understanding of GPU/CUDA fundamentals, multi-GPU systems, and distributed training.

Experience with observability and monitoring tools, particularly Prometheus, Grafana, Loki, Promtail, and CloudWatch.

Experience with AWS/GCP cloud infrastructure and relevant AI/ML services.

Strong understanding of Git/GitHub, REST APIs, and CI/CD pipelines.

Ability to design, build, deploy, and monitor scalable AI/ML systems and production inference services.

Location: Chennai, Hybrid work mode (4 days Work from Office and 1 day Work from Home)

Rewards:

Attractive compensation

Rapid and clear growth path

Interaction with industry experts

On job training and skill development

Eucloid offers an expedited growth path along with a compensation package which is among the best in the industry.

Additional Information: If you require reasonable accommodation during the application or selection process, please do not hesitate to reach out to Eucloid HR at [email removed]