Role & responsibilities

- Define AI/ML architecture, model-selection strategy, reusable service patterns and technical standards across products

- Lead design of LLM workflows, retrieval systems, tool use, routing, structured outputs and human-review controls

- Guide predictive-model development for classification, ranking, recommendation, forecasting and anomaly detection

- Set up dataset, experimentation, offline evaluation, online measurement and model-release practices

- Define quality, safety, latency and cost thresholds; ensure changes are validated before release

- Design MLOps/LLMOps covering versioning, deployment, tracing, drift, rollback and provider resilience

- Review AI designs and code, build reference implementations and resolve complex production issues

- Mentor AI/ML engineers and coordinate with backend, frontend, QA, security and cloud teams

- Evaluate external models and platforms while protecting data and controlling cost and vendor dependence

- Communicate evidence, trade-offs and model limitations clearly to leadership

Preferred candidate profile

- 7 - 10 years in applied AI/ML, including leading production ML or LLM-enabled systems

- Advanced Python with strong foundations in ML, deep learning, statistics and experiment design

- Proven ownership of production LLM apps (structured outputs, tool calling,



retrieval, evaluation)

- Hands-on with scikit-learn, PyTorch, TensorFlow or similar for classical ML systems

- Strong grasp of precision, recall, ranking metrics, calibration, drift, bias, data leakage and error analysis

- Experience with model deployment, versioning, data pipelines, observability and rollback

- Able to architect secure AI services (prompt injection, unsafe tool use, data leakage defences)

- Solid SQL and analysis skills on event, behavioural or time-series data

- Proven technical leadership, mentoring and design-review experience

Good to have

- Multi-model routing, agent orchestration, vector retrieval, knowledge graphs

- Causal inference, uplift modeling, recommendations, optimization or contextual bandits

- Multimodal models (text, image, audio, video)

- Cloud ML platforms, Kubernetes, distributed inference, GPU optimization

- Building shared AI platforms used by multiple product teams

Why Join Us

- Senior ownership of AI strategy and architecture across multiple digital products

- Stay hands-on while building and mentoring a growing AI/ML team

- Turn ambiguous business problems into measurable, production-grade AI

- Shape reusable AI foundations that scale across products

📌 Senior AI/ML Engineer (Noida)
🏢 Juuuno
📍 Noida