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