Job description

Design, build and ship production AI applications end-to-end — agentic assistants, RAG systems, document intelligence, voice agents, predictive/forecasting models, and workflow-automation agents.
Fine-tune and adapt open-weight LLMs (Qwen, Llama, Mistral, Phi, Gemma) for company-specific use cases; own the training loop, evaluation and rollout.
Build multi-agent systems that integrate across enterprise platforms — Oracle ERP, HRMS, Google/Microsoft Calendar, email, LinkedIn, WhatsApp Business, webhooks and custom APIs.
Own the AI stack architecture: model selection and routing, evaluation harnesses, guardrails, observability, cost and latency optimisation, security and governance.
Establish MLOps / LLMOps practices — evals, tracing, versioning, rollback, human-in-the-loop feedback capture.

Skills:

Fine-tuning open-weight LLMs — Qwen (including larger variants such as Qwen 2.5 32B / 72B), Llama, Mistral, Phi, Gemma — using LoRA, QLoRA and PEFT.
Preference alignment techniques — DPO, ORPO, and awareness of RLHF-style approaches.
GPU-based training and inference — A100 / H100 / L40S; distributed training basics; quantisation (GPTQ, AWQ, GGUF); memory and throughput optimisation.
Self-hosted inference stacks — vLLM, Ollama, llama.cpp, TGI.
Working with Small Language Models (SLMs) for cost-efficient, specialised tasks.
Multi-model routing and abstraction using LiteLLM or equivalent.
Managed AI platforms — Azure OpenAI, AWS Bedrock, Google Vertex AI — and clear judgment on when to build vs buy vs host.

Someone shared one job with you. ASAI keeps finding the rest.

Scored, not searched. Every role ranked against your actual profile.

Alerts as often as hourly. Reach new roles while the pile is still small.

Skill gaps, spelled out. See exactly which requirements you don't meet yet.

Verified jobs, only. Say no to ghost jobs. Your time deserves respect.

Someone shared one job with you. ASAI keeps finding the rest.

Scored, not searched. Every role ranked against your actual profile.

Alerts as often as hourly. Reach new roles while the pile is still small.

Skill gaps, spelled out. See exactly which requirements you don't meet yet.

Verified jobs, only. Say no to ghost jobs. Your time deserves respect.

Job description

Design, build and ship production AI applications end-to-end — agentic assistants, RAG systems, document intelligence, voice agents, predictive/forecasting models, and workflow-automation agents.
Fine-tune and adapt open-weight LLMs (Qwen, Llama, Mistral, Phi, Gemma) for company-specific use cases; own the training loop, evaluation and rollout.
Build multi-agent systems that integrate across enterprise platforms — Oracle ERP, HRMS, Google/Microsoft Calendar, email, LinkedIn, WhatsApp Business, webhooks and custom APIs.
Own the AI stack architecture: model selection and routing, evaluation harnesses, guardrails, observability, cost and latency optimisation, security and governance.
Establish MLOps / LLMOps practices — evals, tracing, versioning, rollback, human-in-the-loop feedback capture.

Skills:

Fine-tuning open-weight LLMs — Qwen (including larger variants such as Qwen 2.5 32B / 72B), Llama, Mistral, Phi, Gemma — using LoRA, QLoRA and PEFT.
Preference alignment techniques — DPO, ORPO, and awareness of RLHF-style approaches.
GPU-based training and inference — A100 / H100 / L40S; distributed training basics; quantisation (GPTQ, AWQ, GGUF); memory and throughput optimisation.
Self-hosted inference stacks — vLLM, Ollama, llama.cpp, TGI.
Working with Small Language Models (SLMs) for cost-efficient, specialised tasks.
Multi-model routing and abstraction using LiteLLM or equivalent.
Managed AI platforms — Azure OpenAI, AWS Bedrock, Google Vertex AI — and clear judgment on when to build vs buy vs host.