Description

Position Overview: We are looking for AI Solutions Engineer to lead the design, development, and deployment of AI/ML-powered solutions within an enterprise environment. This is a hands-on role focused on applied AI — from deep learning and classical ML to LLM integration and agentic systems — with a strong emphasis on delivering production-ready solutions that solve real business problems. The candidate will proactively identify opportunities where AI can add value, build proof-of-concepts, and take them to production. During early phases they may also contribute to building internal AI-powered developer productivity tools (code documentation agents, Copilot integrations, automated testing) to deliver value from day one. Key Responsibilities AI/ML Solution Design and Development • Identify business problems suited for AI/ML and design end-to-end solution architectures • Build, train, evaluate, and deploy ML models using Python — covering supervised/unsupervised learning, deep learning, and reinforcement learning as appropriate • Implement deep learning solutions (CNNs, RNNs/LSTMs, Transformers) using PyTorch or TensorFlow for NLP, computer vision, or structured data problems • Design and build RAG systems, AI agent workflows, and LLM integrations with proper prompt engineering, guardrails, and evaluation • Develop and maintain ML pipelines: data ingestion, feature engineering, model training, versioning, and deployment LLM and Generative AI • Integrate LLM APIs (OpenAI, Anthropic Claude, Google Gemini, open-source models) into enterprise applications • Build agentic systems using frameworks like LangChain, LangGraph, or CrewAI — including tool use, multi-agent orchestration, and MCP integration • Implement vector databases, embedding pipelines, and semantic search for RAG systems • Apply advanced prompt engineering techniques (chain-of-thought, ReAct, few-shot, structured outputs) to optimize LLM performance MLOps and Production Delivery • Establish experiment tracking, model versioning, and reproducibility practices (MLflow, Weights & Biases, or equivalent) • Deploy and serve models in production with monitoring, drift detection, and automated retraining where needed • Optimize model inference for latency and cost — quantization, distillation, and efficient serving strategies • Build APIs and microservices to expose AI capabilities to consuming applications Technical Leadership • Mentor teams on AI/ML best practices, integration patterns, and responsible AI principles • Collaborate with product managers and business stakeholders to translate ambiguous requirements into AI solutions • Drive AI adoption through documentation, tech talks, and hands-on workshops

Skills Required:
LLM, AI, Google Cloud Platform - Biq Query, Data Flow, Dataproc, Data Fusion, TERRAFORM, Tekton,Cloud SQL, AIRFLOW, POSTGRES, Airflow PySpark, Python, API

Skills Preferred:
API

Experience Required:
Experience • 5+ years of software development experience with strong engineering fundamentals • 3+ years of hands-on AI/ML experience — model development, training, and production deployment • 2+ years working with LLMs and generative AI in real-world applications • Proven track record of taking AI/ML projects from prototype to production

Experience Preferred:
AI/ML and Deep Learning • Strong foundation in ML: supervised and unsupervised learning, classification, regression, clustering, ensemble methods, and model evaluation techniques • Hands-on deep learning experience: CNNs, RNNs/LSTMs, Transformers, and attention mechanisms • Proficiency in Python for ML — NumPy, pandas, scikit-learn, and PyTorch or TensorFlow • Understanding of NLP: tokenization, embeddings, text classification, named entity recognition, and sequence-to-sequence models • Knowledge of neural network optimization: gradient descent variants, regularization (dropout, batch normalization), and hyperparameter tuning • Familiarity with reinforcement learning concepts and their applications LLM and Generative AI • Experience integrating LLMs (GPT, Claude, Gemini, LLaMA, Mistral) into applications • Prompt engineering: zero-shot, few-shot, chain-of-thought, ReAct, and structured output patterns • Experience building RAG systems — document chunking, embedding models, vector search, and retrieval optimization • Working knowledge of AI/LLM orchestration frameworks (LangChain, LlamaIndex, LangGraph, or equivalent) • Understanding of AI agent patterns, tool use, and agentic workflows Engineering and Cloud • Strong software engineering fundamentals — clean code, design patterns, API design, testing practices • Experience building and deploying production services (RESTful APIs, microservices) • Proficiency with at least one major cloud platform (AWS, GCP, or Azure) and cloud ML services • Experience with Docker, CI/CD pipelines, and Git-based workflows • Working knowledge of databases (SQL and NoSQL) and vector databases Soft Skills • Solution-oriented mindset — proactively identifies AI opportunities rather than waiting for requirements • Ability to communicate complex AI/ML concepts to non-technical stakeholders • Comfortable with ambiguity and rapid technology evolution • Mentoring and technical leadership ability

Education Required:
Bachelor's Degree

Education Preferred:
Certification Program

Key Skills

Education

Any Graduate