Senior AI/ML Engineer
- Posted: March 19, 2026
- Location: Remote (India Preferred)
- Experience: 4+ years in AI/ML engineering
- Type: Full-Time
What You Will Build
At lowtouch.ai, the models you build run in production. Our enterprise clients — global IT services companies, GCCs, and large enterprises — depend on the AI systems we deploy to automate mission-critical workflows with Human-in-the-Loop controls. This is not a research role. You will own models from training through production monitoring, and you will see the business impact of your work within weeks of joining.
You will work on:
- Large Language Model fine-tuning and prompt engineering for domain-specific enterprise tasks
- Retrieval-Augmented Generation (RAG) pipelines over private, air-gapped enterprise data
- Agentic AI systems: multi-step workflows where subagents built by the lowtouch.ai team execute tasks with developer commit reviews and lead approvals at each stage
- Model serving infrastructure for private on-premise and cloud deployments
- Evaluation frameworks to measure accuracy, latency, and safety across production deployments
About lowtouch.ai
lowtouch.ai is a no-code agentic AI platform for enterprise automation. Enterprises deploy production-ready AI agents in 4 to 6 weeks using our platform, without writing infrastructure code. We are SOC 2 Type II and ISO 27001 certified, and every deployment runs on private infrastructure: no enterprise data touches shared cloud services.
Our clients are CTOs, CIOs, and IT leaders at Enterprises, GCCs, and IT Services companies who need AI that works inside their security perimeter. The models our engineers build are the core of that promise.
Why Join Us?
Ship models that matter. Every model you deploy handles real enterprise workloads: AMS payment monitoring, RFP generation, migration orchestration, and more. You will see adoption metrics within weeks, not quarters.
Own your stack. From dataset curation to inference optimization to production alerting, senior engineers here own their full surface area. There are no hand-off queues.
Private AI is the growth market. Enterprise demand for private, on-premise AI is accelerating sharply as data governance requirements tighten globally. You will build deep expertise in the infrastructure category that is winning.
Direct access to leadership. You will work closely with our engineering leads and the founding team. Your technical decisions shape the product roadmap.
Remote-first, flexible. We measure output, not hours. Work from wherever you do your best engineering.
Key Responsibilities
Model Development and Deployment
- Design, train, fine-tune, and deploy ML and LLM models for enterprise automation use cases
- Build and optimize RAG pipelines over structured and unstructured enterprise data sources
- Develop agentic workflow components: planning, tool use, memory, and error recovery
- Implement model evaluation frameworks covering accuracy, hallucination rate, latency, and cost
MLOps and Infrastructure
- Build model serving pipelines for private cloud and on-premise deployments using Docker and Kubernetes
- Establish monitoring and alerting for model performance drift in production
- Optimize inference throughput and latency for cost-effective enterprise deployments
- Maintain reproducible training pipelines with versioned datasets and experiment tracking
Collaboration and Quality
- Work with product and solutions teams to translate enterprise requirements into model specifications
- Conduct code and model reviews as part of the HITL-driven engineering process
- Document models, datasets, and deployment configurations thoroughly for enterprise audit readiness
- Stay current with LLM and agentic AI research; evaluate new techniques for production applicability
Qualifications
Required:
- 4+ years of hands-on experience in ML/AI engineering, with at least 2 years working on LLMs or generative AI systems in production
- Strong Python skills; experience with PyTorch or TensorFlow
- Demonstrated experience building and deploying RAG pipelines or LLM applications
- Familiarity with vector databases (Pinecone, Weaviate, Chroma, or similar)
- Experience with model serving frameworks (TGI, vLLM, Triton, or similar)
- Solid understanding of MLOps practices: experiment tracking (MLflow/W&B), model versioning, CI/CD for ML
- Experience deploying models on Kubernetes or similar container orchestration platforms
Strong Advantage:
- Experience with agentic AI frameworks (LangGraph, CrewAI, AutoGen, or similar)
- Knowledge of LLM fine-tuning techniques (LoRA, QLoRA, RLHF, DPO)
- Exposure to private/on-premise AI deployments and enterprise data security requirements
- Familiarity with NVIDIA GPU infrastructure (A100, H100) for inference optimization
- Background in enterprise software (ITSM, ERP, CRM integrations)
Benefits and Perks
- Competitive salary benchmarked to senior engineering roles in the enterprise AI market
- Remote-first, flexible work environment with no time-tracking
- Direct exposure to enterprise AI deployments across multiple verticals and geographies
- Access to GPU compute for model development and experimentation
- Learning budget for conferences, certifications, and technical courses
- Work with a team that ships to production fast and iterates based on real enterprise feedback
How to Apply
Send your resume and a brief note on the most complex ML system you have shipped to production to careers@ecloudcontrol.com
Subject: Senior AI/ML Engineer — lowtouch.ai
