: 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.
- Solid 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.

📌 Artificial Intelligence Engineer (Nagpur)
🏢 Nice Software Solutions
📍 Nagpur