ML Ops Engineer — Agentic AI Lab (Founding Team)

4+ years in MLOps, ML platform engineering, or infra-focused ML roles

Deep familiarity with model lifecycle management tools: MLflow, Weights & Biases, DVC,

HuggingFace Hub

Experience with large model deployments (open-source LLMs preferred): LLaMA,

Mistral, Falcon, Mixtral

Comfortable with tuning libraries (HuggingFace Trainer, DeepSpeed, FSDP, QLoRA)

Familiarity with inference serving: vLLM, TGI, Ray Serve, Triton Inference Server

Proficient with Terraform, Helm, K8s, and container orchestration

Experience with CI/CD for ML (e.g. GitHub Actions + model checkpoints)

Managed hybrid workloads across GPU cloud (Lambda, Modal, HuggingFace Inference,

Sagemaker)

Familiar with cost optimization (spot instance scaling, batch prioritization, model sharding)

Agent + Data Pipeline Support: ●

Familiarity with LangChain, LangGraph, LlamaIndex or similar RAG/agent orchestration tools

Built embedding pipelines for multi-source documents (PDF, JSON, CSV, HTML)

Integrated with vector databases (Weaviate, Qdrant, FAISS, Chroma)

Implemented model-level RBAC, usage tracking, audit trails

Integrated with API rate limits, tenant billing, and SLA observability

Experience with policy-as-code systems (OPA, Rego) and access layers

LLM Ops : HuggingFace, DeepSpeed, MLflow, Weights & Biases, DVC

Infra : Kubernetes (GKE/EKS), Ray, Terraform, Helm, GitHub Actions, ArgoCD

Serving : vLLM, TGI, Triton, Ray Serve

Pipelines : Prefect, Airflow, Dagster

Monitoring : Prometheus, Grafana, OpenTelemetry, LangSmith

Security : OPA (Rego), Keycloak, Vault

Languages : Python (primary), Bash, optionally Rust or Go for tooling

Builder's mindset with startup autonomy: you automate what slows you down

Obsessive about reproducibility , observability , and traceability

Comfortable with a hybrid team of AI researchers, DevOps, and backend engineers

Interested in aligning ML systems to product delivery, not just papers

Bonus: experience with SOC2, HIPAA, or GovCloud-grade model operations

5+ years as a full stack or backend engineer

Experience owning and delivering production systems end-to-end

Prior experience with modern frontend frameworks (React, Next.js)

Familiarity with building APIs, databases, cloud infrastructure, or deployment workflows at scale

Comfortable working in early-stage startups or autonomous roles, prior experience as a founder, founding engineer, or a 0-1 pre-seed startup is a big plus

Comfortable with ambiguity, eager to prototype and iterate quickly

Strong sense of ownership — prefers to build systems rather than wait for tickets

Enjoys thinking about architecture, performance, and tradeoffs at every level

Clear communicator and pragmatic team player

Values equity and impact over prestige or hierarchy

Prior startup or founding team experience

Your work will enable models and agents to be trained, evaluated, deployed, and governed at

scale — across many tenants, models, and tasks. This is the backbone of a secure, reliable,

and scalable AI-native enterprise system. If you dream about using AI to solve some really hard

real world problems – we would love to hear from you.