Senior AI Full-Stack Developer

4 days ago

Chennai, Tamil Nadu, India Xerago Full time ₹ 8,00,000 - ₹ 24,00,000 per year

Roles & Responsibilities:

  • Architect scalable AI solutions:
    Define end-to-end reference architectures (LLM/RAG, NLP, vision, agentic workflows) that move cleanly from
    DemoBytes → POC → MVP → Demoable → Production
    .
  • Own full-stack delivery:
    Build features across data/ML, backend APIs/services (FastAPI/Flask), and lightweight UIs ) for demoable, user-ready outputs.
  • Rapid prototyping:
    Stand up POCs in days; harden validated solutions into MVP and production with incremental quality/security gates.
  • MLOps & platformization:
    Implement CI/CD/CT for models, datasets, prompts; automate evals, canary/rollback, versioning, model/data drift monitoring, and experiment tracking (W&B/MLflow).
  • Integration & interoperability:
    Embed AI into existing products and workflows via APIs, queues, SDKs, and webhooks with clear SLAs and observability.
  • Operate what you build:
    Instrument services, track p95 latency/availability/cost, and drive continuous improvement post-launch.
  • Mentor & uplift:
    Coach engineers on best practices (prompting, vector design, evals, latency/cost tuning, secure data handling).
  • Release cadence:
    Maintain
    monthly demo releases
    and
    production releases every two months
    with ALM-driven governance.
  • Ethical AI & compliance:
    Apply privacy-by-design, bias testing/mitigation, model cards, auditability, and data protection controls; ensure documentation in ALM.
  • Trendwatching:
    Track state-of-the-art AI (models, toolchains, infra) and pragmatically incorporate breakthroughs into roadmaps.

Qualifications:

  • 4–6 years
    delivering AI/ML features
    to production
    with fast
    POC → MVP → Production
    cycles.
  • Strong ML/DL fundamentals; hands-on with
    PyTorch
    and/or
    TensorFlow/Keras
    ; LLMs (prompting, fine-tuning/LoRA), RAG patterns, and evaluation.
  • Python
    proficiency; scikit-learn, spaCy/NLTK;
    Hugging Face
    (Transformers/Datasets/PEFT); familiarity with
    YOLO
    /FastAI (role-relevant).
  • Backend engineering for production (FastAPI/Flask), auth, caching, testing; practical

    for demoable UIs.
  • MLOps:
    Docker/Kubernetes, CI/CD (GitHub Actions/Azure DevOps/Jenkins), experiment tracking (
    Weights & Biases
    /MLflow), monitoring (Prometheus/Grafana/OpenTelemetry).
  • Data & storage: SQL/NoSQL (Postgres, Redis), object stores; vector DBs (FAISS/Milvus/pgvector) and retrieval design.
  • Cloud: AWS/Azure/GCP with cost/latency/performance trade-off literacy.
  • AI productivity tools (required):
    Cursor, Windsurf, Claude, Copilot
    for accelerated prototyping, code gen/review, and prompt workflows.
  • Effective communication; crisp documentation and governance in ALM.
  • Working knowledge of ethical AI and
    data protection
    (PII handling, access controls, audit trails).

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