ML Systems Engineer

Location: Pune, India

Overview

We are hiring an ML Systems Engineer to design and deliver cutting-edge AI solutions for enterprise clients at the frontier of agentic AI, inference engineering, and ML systems architecture. You will go beyond applied ML - dissecting how AI systems are built, optimized, and scaled - designing production-grade architectures spanning retrieval systems, inference pipelines, and agentic workflows. You will translate state-of-the-art capabilities into robust, performant solutions, operating at the intersection of ML research awareness and engineering discipline.

Key Responsibilities

• Design and deliver production-grade AI systems for enterprise clients spanning agentic workflows, LLM inference pipelines, and retrieval-augmented architectures.

• Lead ML systems architecture decisions - model serving topology, inference backend selection, KV cache management, batching strategies, and memory optimization - alongside ML performance engineering to profile bottlenecks, benchmark throughput/latency, and evaluate quantization strategies (GPTQ, AWQ, GGUF).

• Architect RAG pipelines and agentic AI systems - from chunking, embedding, hybrid retrieval, and re-ranking through to multi-agent orchestration, tool use, and memory architectures.

• Evaluate frontier model capabilities - reasoning models, multimodal systems, fine-tuned variants - and make principled architectural trade-off decisions for client contexts.

• Build reusable accelerators, reference implementations, and evaluation/observability frameworks encoding best practices across engagements.

• Contribute to technical solutioning - architecture designs, proof-of-concepts, and feasibility assessments - in client-facing contexts.

  Core Requirements

• Python ML ecosystem: Strong programming skills with production AI system experience; hands-on with the PyTorch ecosystem including Hugging Face Transformers, PEFT, Accelerate, and Datasets.

• LLM inference serving: Deep knowledge of KV cache mechanics, quantization, and batching; hands-on with at least one inference runtime (vLLM, TGI, TensorRT-LLM, SGLang, or similar).

• Hands-on experience supporting AI/ML and LLM inference platforms at scale, including working with vLLM for high-performance LLM serving, optimization, and large-scale inference.

• RAG Agentic Systems: Experience designing retrieval architectures and building agentic systems using LangGraph, LlamaIndex Workflows, AutoGen, or CrewAI - including tool use, memory, and multi-agent coordination.

• LLM APIs prompt engineering: Strong grasp of structured output generation, function calling, and provider SDK usage across OpenAI, Anthropic, Mistral, Hugging Face, and similar.

• Deployment fundamentals: Proficiency with Docker, containerization, and Linux environments for packaging, deploying, and debugging AI systems.

• Comfortable leveraging AI-assisted tools for collaborative development, code generation, refactoring, and productivity enhancement.

  Preferred

• Fine-tuning: Experience with LoRA/QLoRA, dataset curation, and instruction tuning; understanding of when fine-tuning is the right lever vs. prompting or RAG. Emergys

• Low-level AI systems: Familiarity with CUDA, Triton, or similar GPU programming models; working knowledge of C++ or Rust.

• Infrastructure observability: Kubernetes for containerized AI workloads; experience with LangSmith, Arize, WB, Phoenix, or Prometheus/Grafana for ML observability.

  Ways to Stand Out From the Crowd

• You have built and deployed a production agentic system and can speak to the failure modes and design decisions that only emerge at runtime.

• You have done inference optimization at a systems level - tuning serving infrastructure, implementing custom batching logic, or optimizing a quantization pipeline to hit real SLAs.

• You have open-source contributions to prominent ML systems repositories - vLLM, SGLang, llama.cpp, TGI, LangChain, LlamaIndex, or similar - demonstrating work that holds up to community scrutiny.

• You have designed custom LLM evaluation frameworks with structured regression harnesses, domain-specific evals, or human-in-the-loop feedback loops - beyond off-the-shelf metrics.

• You bring a client-facing engineering mindset and can defend opinions on reasoning models, long-context retrieval, or inference hardware tradeoffs based on hands-on experimentation.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.