Lead ML Engineer

Role: AI/ML Lead

Experience: 7-11 years

Designation: Associate Architect

Location: Hyderabad & Pune

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GEN AI & FINE TUNING OF LLMs IS MUST

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Key Responsibilities:

Architecture & Infrastructure

• Design, implement, and optimize end-to-end ML training workflows including infrastructure setup, orchestration, fine-tuning, deployment, and monitoring.
• Evaluate and integrate multi-cloud and single-cloud training options across AWS and other major platforms.
• Lead cluster configuration, orchestration design, environment customization, and scaling strategies.
• Compare and recommend hardware options (GPUs, TPUs, accelerators) based on performance, cost, and availability.

Technical Expertise Requirements

• At least 4-5 years in AI/ML infrastructure and large-scale training environments.
• Expert in AWS cloud services (EC2, S3, EKS, SageMaker, Batch, FSx, etc.) and familiar with Azure, GCP, and hybrid/multi-cloud setups.
• Strong knowledge of AI/ML training frameworks (PyTorch, TensorFlow, Hugging Face, DeepSpeed, Megatron, Ray, etc.).
• Proven experience with cluster orchestration tools (Kubernetes, Slurm, Ray, SageMaker, Kubeflow).
• Deep understanding of hardware architectures for AI workloads (NVIDIA, AMD, Intel Habana, TPU).

LLM Inference Optimization

• Expert knowledge of inference optimization techniques including speculative decoding, KV cache optimization (MQA/GQA/PagedAttention), and dynamic batching.
• Deep understanding of prefill vs decode phases, memory-bound vs compute-bound operations.
• Experience with quantization methods (INT4/INT8, GPTQ, AWQ) and model parallelism strategies.

Inference Frameworks

• Hands-on experience with production inference engines: vLLM, TensorRT-LLM, DeepSpeed-Inference, or TGI.
• Proficiency with serving frameworks: Triton Inference Server, KServe, or Ray Serve.
• Familiarity with kernel optimization libraries (FlashAttention, xFormers).

Performance Engineering

• Proven ability to optimize inference metrics: TTFT (first token latency), ITL (inter-token latency), and throughput.
• Experience profiling and resolving GPU memory bottlenecks and OOM issues.
• Knowledge of hardware-specific optimizations for modern GPU architectures (A100/H100).

Fine tuning

• Drive end-to-end fine-tuning of LLMs, including model selection, dataset preparation/cleaning, tokenization, and evaluation with baseline metrics.
• Configure and execute fine-tuning experiments (LoRA, QLoRA, etc.) on large-scale compute setups, ensuring optimal hyperparameter tuning, logging, and checkpointing.
• Document fine-tuning outcomes by capturing performance metrics (losses, BERT/ROUGE scores, training time, resource utilization) and benchmark against baseline models.

If you've done only POCs and not production ready ML models which scale, Please skip to apply