Artificial Intelligence Engineer

Job Description: AI Engineer
Department: Artificial Intelligence
Experience: 35 years
Location: Pune /Nagpur (Work From Office)

Role Overview
We are seeking an experienced AI Engineer with 3–5 years of hands-on experience in generative AI, machine learning, MLOps, forecasting, and production-grade AI solution development. The selected candidate will design, build, deploy, monitor, and optimize scalable AI systems. The role requires practical expertise in LangChain, LangGraph, Retrieval-Augmented Generation, LLM orchestration, agentic workflows, machine learning lifecycle management, and real-world forecasting solutions.

Key Responsibilities

• Design, develop, deploy, and maintain production-grade machine learning and generative AI solutions.
• Build LLM-powered applications using LangChain, LangGraph, RAG, tool calling, and agentic workflows.
• Design multi-step AI workflows involving planning, reasoning, retrieval, memory, validation, guardrails, and human approval.
• Develop and optimize machine learning models for classification, regression, predictive analytics, and forecasting.
• Manage the complete machine learning lifecycle, including data preparation, experimentation, validation, deployment, monitoring, and retraining.
• Build scalable model-serving APIs, AI microservices, and application integrations.
• Establish CI/CD pipelines for machine learning and generative AI applications.
• Implement model and dataset versioning, experiment tracking, model registries, governance, and release-management processes.
• Evaluate LLM and RAG systems for relevance, factuality, latency, cost, safety, reliability, and hallucination risk.
• Develop demand, sales, inventory, revenue, capacity, workforce, resource, and time-series forecasting solutions.
• Monitor model performance, data drift, system reliability, and production issues.
• Optimize deployed models and resolve performance, scalability, and accuracy issues.
• Collaborate with product, engineering, data, and business teams to translate requirements into scalable AI solutions.
• Conduct technical design reviews and contribute to system architecture decisions.
• Document solution architecture, model behaviour, assumptions, limitations, risks, and operational procedures.

Qualifications (Required)

• 3–5 years of relevant experience in artificial intelligence, machine learning, data science, applied analytics, or a related field.
• Demonstrated experience developing and deploying AI and machine learning applications in production.
• Hands-on experience with LangChain and LangGraph.
• Strong understanding of Retrieval-Augmented Generation, LLM orchestration, agentic AI workflows, and tool or function calling.
• Experience with prompt design, prompt management, embeddings, vector databases, document chunking, indexing, retrieval, and reranking.
• Experience evaluating LLM outputs and implementing validation, guardrails, observability, and hallucination-reduction techniques.
• Production deployment involving RAG, LLM orchestration, or AI agents.
• Strong experience in end-to-end machine learning model development.
• Proficiency in data preprocessing, feature engineering, model training, validation, evaluation, and hyperparameter optimization.
• Experience with classification, regression, statistical modelling, predictive analytics, time-series modelling, and model explainability.
• Experience deploying trained models and monitoring their post-deployment performance.
• Practical knowledge of model serving, CI/CD for machine learning, experiment tracking, model and dataset versioning, model registries, automated testing, and retraining workflows.
• Understanding of model governance, reproducibility, lifecycle management, performance monitoring, and drift detection.
• Hands-on experience with Docker and cloud-based deployment.
• Experience delivering at least one forecasting or predictive analytics solution.
• Ability to explain forecasting objectives, business context, seasonality, feature engineering, model selection, backtesting, accuracy metrics, outlier treatment, refresh frequency, and business outcomes.
• Strong proficiency in Python and SQL.
• Experience developing REST APIs and microservices using FastAPI or Flask.
• Experience with PyTorch, TensorFlow, or Scikit-learn.
• Strong understanding of software engineering, debugging, system design, and production deployment practices.
• Experience working with cross-functional product, engineering, data, and business stakeholders.
• Strong analytical thinking, problem-solving, technical communication, and documentation skills.

Preferred Skills

• Experience with MLflow, Kubeflow, Databricks, Azure Machine Learning, AWS SageMaker, or equivalent MLOps platforms.
• Experience with Kubernetes and container orchestration.
• Familiarity with AWS, Microsoft Azure, or Google Cloud Platform.
• Experience with Databricks or Microsoft Fabric.
• Knowledge of relational and NoSQL databases.
• Experience with vector databases and semantic search platforms.
• Familiarity with Git, automated testing frameworks, and CI/CD tools.
• Experience with monitoring, observability, and logging platforms.
• Knowledge of scalable distributed systems and cloud-native AI architecture.
• Experience optimizing LLM applications for cost, latency, throughput, and reliability.
• Experience implementing human-in-the-loop review and approval workflows.
• Demonstrated success in delivering AI solutions with measurable business impact.
• Experience conducting technical design reviews or contributing to architecture decisions.