Principal Machine Learning Engineer (Speech AI and LLM Agents)

Job Title: Principal Machine Learning Engineer (Speech AI and LLM Agents) Company: Blessing Softtech Job Category: Machine Learning Roles & Responsibilities: The Role What You Will Do * Own the full realtime speech loop: streaming ASR, LLM inference and streaming TTS, with hard p50 and p95 latency budgets for every hop. * Design, train and ship turn\-taking, endpointing, barge\-in and audio emotion models where off\-the\-shelf systems fall short. * Build agent systems that compile a natural\-language brief into a tested production agent: tool use, structured state graphs, automated simulation, scoring and self\-repair. * Architect multi\-party, multilingual realtime audio: per\-speaker streams, live speech translation, and floor control between humans and agents. * Implement agent\-to\-agent communication on open protocols (A2A, MCP) with verified identity, scoped permissions and loop protection. * Build the evaluation and data flywheel: golden sets per language, LLM\-as\-judge calibrated against human labels, regression gates in CI, consent\-aware labelling and PII redaction. * Fine\-tune, distil, quantise and serve open\-weight LLM and speech models, on cloud GPUs and on edge hardware that runs with no network. * Automate MLOps end to end: training pipelines, model registry, canary releases, auto rollback, and cost and latency guardrails per tenant. * Hire, mentor and lead a team of 3 to 8 ML engineers. Work Location: Pune/Pimpri-Chinchwad Area Availability: Full time

* 7\+ years building ML systems, with 3\+ years shipping LLM or speech systems to real users at scale. * A latency\-critical streaming system you shipped, and the numbers to prove it: p50, p95, cost per unit, failure modes. * Expert Python and PyTorch. Strong command of transformer internals: attention variants, KV cache, decoding and sampling strategies. * Hands\-on speech experience: Conformer and Whisper\-class ASR, neural codec or flow\-matching TTS, VAD, audio front ends, Opus and telephony sample rates. * LLM serving and optimisation: vLLM, TensorRT\-LLM or equivalent; continuous batching, speculative decoding, INT8 and INT4 quantisation (AWQ, GPTQ). * Training and alignment: SFT, LoRA and QLoRA, DPO or other preference optimisation, distillation; evals that predict production outcomes. * Production infrastructure: Kubernetes, AWS, GPU scheduling, tracing and observability. * You write clearly, turn ambiguity into a spec, and ship. Key Skills: Machine Learning, Python, PyTorch Salary: Not disclosed