AI Solutions Engineer

The AI Solutions Engineer will contribute to the development of innovative AI products and internal enterprise solutions. The role will involve converting business requirements into practical AI solutions, integrating AI capabilities with enterprise systems, and supporting the complete lifecycle of AI and machine learning applications. The successful candidate will be expected to balance innovation with engineering discipline by applying best practices in software development, cloud architecture, security, governance, testing, monitoring, and operational reliability.

• Design, develop, train, evaluate, and deploy machine learning and deep learning models.
• Build Generative AI solutions using models such as GPT, Claude, Gemini, Llama, and Mistral.
• Develop Retrieval-Augmented Generation systems, AI agents, intelligent automation, and enterprise LLM applications.
• Apply prompt engineering, fine-tuning, model customization, vector embeddings, and semantic search.
• Develop solutions for natural language processing, predictive analytics, forecasting, recommendation systems, and intelligent decision support.
• Build production-grade AI applications, APIs, microservices, and enterprise software using Python and modern engineering practices.
• Integrate AI services with enterprise applications, data platforms, databases, and business systems.
• Design and implement MLOps and LLMOps pipelines for model development, testing, deployment, monitoring, and lifecycle management.
• Deploy AI solutions on AWS, Azure, or Google Cloud using cloud-native services and scalable architectures.
• Work with containers, Kubernetes, serverless technologies, and real-time inference platforms.
• Monitor model performance, data drift, hallucinations, latency, cost, security, and operational metrics.
• Apply responsible AI, privacy, security, explainability, bias mitigation, compliance, and governance practices.
• Collaborate with data scientists, architects, developers, DevOps engineers, and business stakeholders.
• Contribute to technical documentation, coding standards, automated testing, CI/CD, and continuous improvement.

AI and Machine Learning Development

• Develop and deploy machine learning and deep learning models for business applications.
• Evaluate model accuracy, performance, scalability, latency, and cost.
• Select appropriate algorithms, models, frameworks, and deployment approaches.
• Build reliable, maintainable AI solutions suitable for production use. Generative AI and Agentic AI
• Design and implement LLM-powered applications using commercial and open-source models.
• Develop RAG pipelines using enterprise documents, structured data, and knowledge repositories.
• Build AI agents and multi-agent workflows for business process automation.
• Work with prompt engineering, fine-tuning, embeddings, semantic search, and model evaluation.
• Explore emerging protocols and frameworks, including MCP and A2A, where appropriate.

Software Engineering

• Develop clean, modular, secure, and testable code primarily in Python.
• Build APIs and microservices using FastAPI, Flask, or comparable frameworks.
• Apply software design principles, data structures, algorithms, version control, and automated testing.
• Integrate AI solutions with enterprise systems, databases, APIs, and cloud services.
• Support applications developed using Java, JavaScript, ReactJS, or related technologies.

MLOps, LLMOps, and DevOps

• Build automated pipelines for training, validation, testing, deployment, and monitoring.
• Use tools such as MLflow, Kubeflow, Azure ML, or Amazon SageMaker.
• Implement model versioning, experiment tracking, monitoring, rollback, and lifecycle management.
• Support CI/CD automation using GitHub, Azure DevOps, Jenkins, or similar platforms.
• Monitor production systems for reliability, performance, security, data drift, and model quality.

Cloud and Platform Engineering

• Develop and deploy AI solutions on AWS, Azure, and Google Cloud.
• Work with AWS Bedrock, Amazon SageMaker, Azure OpenAI Service, Google Vertex AI, and related services.
• Build scalable architectures using Docker, Kubernetes, serverless technologies, and cloud-native services.
• Optimize AI workloads for performance, availability, security, and cost efficiency.

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related technical discipline.
• At least 5 years of experience in software engineering, machine learning development, data engineering, or a related field.
• At least 2 years of hands-on experience building and deploying Generative AI, Agentic AI, or AI/ML solutions in production environments.
• Strong programming skills in Python.
• Experience with Java, JavaScript, ReactJS, or comparable programming technologies.
• Strong understanding of algorithms, data structures, APIs, software design, and application development.
• Practical experience with machine learning, deep learning, model training, evaluation, optimization, and deployment.
• Hands-on experience with LLMs, SLMs, prompt engineering, RAG, AI agents, embeddings, semantic search, and fine-tuning.