FSE Sr.AI Engineer

Key Skills

• Hands‑on experience with Git Hub Spec Kit and spec‑driven development using AI agents (/specify, /plan, /tasks workflow).
• Production‑grade applications built with React / JavaScript frameworks and Node.js REST/GraphQL APIs.
• AWS infrastructure (Lambda, S3, EC2, API Gateway) paired with MongoDB and/or PostgreSQL at scale.

• Lead spec‑first development initiatives using Git Hub Spec Kit — authoring specs, technical plans, and agent‑ready task breakdowns before writing any code.
• Design and build full‑stack web applications using React, JavaScript/Type Script frameworks, and Node.js, from UI to backend API layer.
• Develop, integrate, and maintain RESTful and GraphQL APIs, ensuring performance, reliability, and security across services.
• Architect and deploy cloud‑native solutions on AWS (Lambda, EC2, S3, API Gateway, RDS, Cloud Formation) with a focus on scalability and cost efficiency.
• Build and integrate AI‑powered features — leveraging LLMs, AI agents, prompt engineering, and the GenAI ecosystem to enhance product capabilities.
• Design and manage relational (PostgreSQL) and document (MongoDB) databases, including schema design, query optimisation, and data migrations.
• Collaborate with product managers, designers, and AI/ML engineers to translate requirements into well‑specified, shippable software.
• Participate in code reviews, establish engineering best practices, and contribute to a culture of quality and continuous improvement.

• 5+ years of professional experience in full‑stack software development.
• Proven hands‑on experience with GenAI tools and a spec‑first development approach, including Git Hub Spec Kit or equivalent workflows.
• Strong proficiency in React and modern JavaScript / Type Script frameworks (Next.js, Vue, or similar).
• Solid backend development skills with Node.js — building and maintaining production REST or GraphQL APIs.
• Experience deploying and operating applications on AWS — comfortable with core services such as Lambda, EC2, S3, API Gateway, and RDS.
• Practical experience with both MongoDB (document store) and PostgreSQL (relational), including schema design and query tuning.
• Familiarity with AI agent frameworks, LLM APIs (OpenAI, Anthropic, or similar), and prompt engineering techniques.
• Strong understanding of software engineering fundamentals — data structures, system design, testing, and CI/CD practices.
• Bachelor's degree in computer science, Engineering, or equivalent practical experience.

• Supervised Learning
  • Linear regression and logistic regression
  • Decision trees, Random Forest, Gradient Boosting (XGBoost, LightGBM, Cat Boost)
  • Support Vector Machines (SVMs) and kernel methods
  • Neural networks — CNNs, RNNs, LSTMs, and Transformers
  • Classification, regression, and ranking problems
  • Cross‑validation, bias‑variance trade‑off, regularization (L1/L2, dropout)
• Unsupervised Learning
  • Clustering: K‑Means, DBSCAN, Gaussian Mixture Models, hierarchical clustering
  • Dimensionality reduction: PCA, t‑SNE, UMAP
  • Autoencoders and variational autoencoders (VAEs)
  • Anomaly detection and outlier identification
  • Association rule mining (Apriori, FP‑Growth)
  • Topic modelling (LDA, NMF)
• Reinforcement Learning
  • Markov Decision Processes (MDPs) states, actions, rewards, transitions
  • Model‑free methods: Q‑Learning, SARSA, Deep Q‑Networks (DQN)
  • Policy gradient methods: REINFORCE, PPO, A3C / A2C
  • Actor‑Critic architectures
  • Multi‑armed bandits and contextual bandits
  • Reward shaping, environment design, and simulation frameworks (OpenAI Gym)
• Relevant learning algorithms — Adjacent & advanced techniques
  • Transfer learning and fine‑tuning pre‑trained models
  • Semi‑supervised and self‑supervised learning
  • Active learning and human‑in‑the‑loop pipelines
  • Federated learning for privacy‑preserving training
  • Bayesian optimization and hyperparameter tuning (Optuna, Ray Tune)
  • Ensemble methods, stacking, and model blending
  • Graph Neural Networks (GNNs) a plus
  • Causal inference and counterfactual reasoning — a plus

• Experience with Git Hub Copilot, Cursor, or other AI‑assisted coding environments in day‑to‑day development.
• Familiarity with containerization (Docker, Kubernetes) and infrastructure‑as‑code (Terraform, AWS CDK).
• Exposure to vector databases (Pinecone, pgvector) or RAG (Retrieval‑Augmented Generation) pipelines.
• Knowledge of event‑driven architectures using AWS SQS, SNS, or Event Bridge.
• Experience with Lang Chain, Llama Index, or similar AI orchestration frameworks.
• Contributions to open‑source projects or a portfolio of AI‑integrated applications.
• Familiarity with observability tools — Data Dog, Cloud Watch, or Splunk — for monitoring AI and API workloads.