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CRUTZ LEELA ENTERPRISES

OVERVIEWWe are hiring an ML Systems Engineer to design and deliver cutting-edge AI solutions for enterpriseclients at the frontier of agentic AI, inference engineering, and ML systems architecture. You will gobeyond applied ML - dissecting how AI systems are built, optimized, and scaled - designing production-grade architectures spanning retrieval systems, inference pipelines, and agentic workflows. You willtranslate state-of-the-art capabilities into robust, performant solutions, operating at the intersection of MLresearch awareness and engineering discipline.KEY RESPONSIBILITIES<ul><li>Design and deliver production-grade AI systems for enterprise clients spanning agentic workflows,</li></ul>LLM inference pipelines, and retrieval-augmented architectures.<ul><li>Lead ML systems architecture decisions - model serving topology, inference backend selection, KV</li></ul>cache management, batching strategies, and memory optimization - alongside ML performanceengineering to profile bottlenecks, benchmark throughput/latency, and evaluate quantizationstrategies (GPTQ, AWQ, GGUF).<ul><li>Architect RAG pipelines and agentic AI systems - from chunking, embedding, hybrid retrieval, and re-</li></ul>ranking through to multi-agent orchestration, tool use, and memory architectures.<ul><li>Evaluate frontier model capabilities - reasoning models, multimodal systems, fine-tuned variants - and</li></ul>make principled architectural trade-off decisions for client contexts.<ul><li>Build reusable accelerators, reference implementations, and evaluation/observability frameworks</li></ul>encoding best practices across engagements.<ul><li>Contribute to technical solutioning - architecture designs, proof-of-concepts, and feasibility</li></ul>assessments - in client-facing contexts.TECHNICAL QUALIFICATIONSCore Requirements<ul><li>Python &amp;amp; ML ecosystem: Strong programming skills with production AI system experience; hands-</li></ul>on with the PyTorch ecosystem including Hugging Face Transformers, PEFT, Accelerate, andDatasets.<ul><li>LLM inference &amp;amp; serving: Deep knowledge of KV cache mechanics, quantization, and batching;</li></ul>hands-on with at least one inference runtime (vLLM, TGI, TensorRT-LLM, SGLang, or similar).<ul><li>Hands-on experience supporting AI/ML and LLM inference platforms at scale, including</li></ul>working with vLLM for high-performance LLM serving, optimization, and large-scale inference.<ul><li>RAG &amp;amp; Agentic Systems: Experience designing retrieval architectures and building agentic systems</li></ul>using LangGraph, LlamaIndex Workflows, AutoGen, or CrewAI - including tool use, memory, andmulti-agent coordination.<ul><li>LLM APIs &amp;amp; prompt engineering: Strong grasp of structured output generation, function calling, and</li></ul>provider SDK usage across OpenAI, Anthropic, Mistral, Hugging Face, and similar.<ul><li>Deployment fundamentals: Proficiency with Docker, containerization, and Linux environments for</li></ul>packaging, deploying, and debugging AI systems.<ul><li>Comfortable leveraging AI-assisted tools for collaborative development, code generation, refactoring,</li></ul>and productivity enhancement.Preferred<ul><li>Fine-tuning: Experience with LoRA/QLoRA, dataset curation, and instruction tuning; understanding</li></ul>of when fine-tuning is the right lever vs. prompting or RAG.<ul><li>Low-level AI systems: Familiarity with CUDA, Triton, or similar GPU programming models; working</li></ul>knowledge of C++ or Rust.<ul><li>Infrastructure &amp;amp; observability: Kubernetes for containerized AI workloads; experience with</li></ul>LangSmith, Arize, W&amp;amp;B, Phoenix, or Prometheus/Grafana for ML observability.WAYS TO STAND OUT FROM THE CROWD<ul><li>You have built and deployed a production agentic system and can speak to the failure modes and</li></ul>design decisions that only emerge at runtime.<ul><li>You have done inference optimization at a systems level - tuning serving infrastructure, implementing</li></ul>custom batching logic, or optimizing a quantization pipeline to hit real SLAs.<ul><li>You have open-source contributions to prominent ML systems repositories - vLLM, SGLang,</li></ul>llama.cpp, TGI, LangChain, LlamaIndex, or similar - demonstrating work that holds up to communityscrutiny.<ul><li>You have designed custom LLM evaluation frameworks with structured regression harnesses,</li></ul>domain-specific evals, or human-in-the-loop feedback loops - beyond off-the-shelf metrics.<ul><li>You bring a client-facing engineering mindset and can defend opinions on reasoning models, long-</li></ul>context retrieval, or inference hardware tradeoffs based on hands-on experimentation.Pay: ₹500,000.00 - ₹1,800,000.00 per yearBenefits:<ul><li>Provident Fund</li></ul>Work Location: In person

ML Systems Engineer (Agentic AI & Inference Engineering)

JD - DevOps / Release &amp;amp; Build EngineerExperience: 4&ndash;7 yearsAbout the RoleWe are hiring a DevOps / Release Engineer for our enterprise-grade, high-performance AIinference platform, powered by our RDU (Reconfigurable Dataflow Unit) hardware. Our cloud-agnostic MLOps platform abstracts infrastructure complexity and enables seamlessdeployment, management, and scaling of foundation-model workloads at production scale.You will be part of the build, release, and software-supply-chain backbone of a large polyglot(Go, Rust, Python) Bazel monorepo spanning dozens of components from CI/CD and RemoteBuild Execution through release packaging, Helm/Kubernetes delivery, and security &amp;amp; licensecompliance. This is a role at the intersection of AI infrastructure, enterprise software, andDevOps.Key Responsibilities<ul><li>Keep CI/CD pipelines (CircleCI) stable, fast, and scalable - workflow optimization,</li></ul>configuration management, and reusable orbs/commands.<ul><li>Maintain the Bazel ecosystem (Go, Rust, Python), including RBE setup, caching,</li></ul>troubleshooting, and build-graph health.<ul><li>Maintain and improve our CircleCI setup, including workflow optimization and configuration</li></ul>management.<ul><li>CI release pipeline (nightly and release modes), including multi-image releases driven by</li></ul>manifest/version files.<ul><li>Build and publish artifacts (container images, Helm charts) to GAR / JFrog using tools like</li></ul>crane; manage hotfix delivery to customers.<ul><li>Develop, maintain and run build + smoke validation in the pipeline.</li><li>Partner with engineering teams across the platform to roll out infrastructure changes and</li></ul>best practices.Required Qualifications<ul><li>4+ years in DevOps / Build / Release / Platform engineering.</li><li>Bazel experience in a multi-language monorepo (Go, Rust, and/or Python).</li><li>Hands-on strong CI/CD experience (CircleCI preferred; Jenkins/GitLab CI acceptable),</li></ul>including pipeline-as-code and workflow optimization.<ul><li>Experience with Google Artifact Registry (GAR) and/or JFrog Artifact Management</li><li>Strong scripting/programming skills (Python and Bash).</li><li>Experience with Linux/Unix systems and command-line interfaces</li><li>Experience in managing dependencies in large scale projects</li><li>Problem-solving, ownership mindset and cross-functional collaboration.</li></ul>Preferred Qualifications<ul><li>Familiarity with containerization (e.g., Docker)</li><li>Experience with other CI/CD tools (e.g., Jenkins, GitLab CI/CD, etc.)</li><li>Experience with Python Package Management.</li><li>Google Cloud Platform (GAR, gcp CLI).</li><li>Knowledge of software development best practices and coding standards.</li></ul>Pay: ₹1,000,000.00 - ₹2,000,000.00 per yearBenefits:<ul><li>Provident Fund</li></ul>Work Location: In person

DevOps / Release & Build Engineer

Join CRUTZ LEELA ENTERPRISES and explore 2   exciting career opportunities. They're hiring for various roles with options for remote or on-site work. Apply today and become part of their innovative team.

CRUTZ LEELA ENTERPRISES

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ML Systems Engineer (Agentic AI & Inference Engineering)

DevOps / Release & Build Engineer