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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Python AI/ML Engineer - **Company:** Shree Narayani Networking Solutions Llc - **Location:** Tampa, FL, United States - **Experience:** Expert - **Contract:** Contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Application Frameworks, Build Automation, Automation of Tests, Microsoft Azure, Cloud Computing, Cloud Engineering, Software Quality, Code Review, Continuous Integration, Information Engineering, Software Debugging, DevOps, Github, Monitoring of Systems, JSON, Python (Programming Language), Machine Learning, Performance Tuning, Cloud Services, Tensorflow, Shell Script, Software Engineering, SQL Databases, Systems Integration, Unstructured Data, Web Services, Google Cloud, Enterprise Software Applications, Performance Testing, Feature Engineering, Data Ingestion, Spring Cloud, Pytorch, LangChain, Bitbucket Pipelines, Retrieval-Augmented Generation, Flask (Web Framework), Large Language Models, Snowflake, Prompt Engineering, Apache Spark, Model Validation, Llamaindex, Generative AI, Backend, Git, Fastapi, Pyspark, Scikit Learn, Kubernetes, Information Technology, Data Analytics, Machine Learning Operations, Drift Detection, Api Design, Restful APIs, Semantic Kernel, Software Version Control, Data Pipelines, Docker, Jenkins, Databricks, Vulnerability Analysis, Microservices - **Published:** October 16, 2026 - **Apply:** https://www.dice.com/job-detail/f6c57f52-e514-42a9-8d68-05dd9d89d392 ## About the Role * Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related discipline, or equivalent professional experience. * 8-12 years of software engineering experience, including substantial hands-on Python development. * Production experience designing, deploying, and supporting AI/ML solutions. * Strong understanding of machine learning algorithms, model lifecycle management, and MLOps principles. * Experience developing RESTful APIs, microservices, and system integrations. * Hands-on experience with at least one major cloud platform: AWS, Azure, or Google Cloud Platform. * Demonstrated experience creating and maintaining CI/CD pipelines and applying DevOps practices. * Experience with Docker, Kubernetes, SQL, Git, automated testing, debugging, and production support. * Strong analytical, communication, stakeholder-management, and problem-solving skills., * Experience with Generative AI, large language models, prompt engineering, RAG, embeddings, and vector databases. * Hands-on knowledge of frameworks such as LangChain, LlamaIndex, Semantic Kernel, scikit-learn, TensorFlow, or PyTorch. * Experience with Snowflake, Databricks, Spark, PySpark, MLflow, Kubeflow, or Airflow. * Experience building cloud-native applications using FastAPI or Flask. * Experience in financial services, audit, risk, compliance, or another highly regulated enterprise environment. * Knowledge of responsible AI, model governance, data privacy, security, and enterprise control requirements. Core Technical Skills Category Skills Programming Python, SQL, shell scripting AI/ML and GenAI scikit-learn, TensorFlow, PyTorch, LLMs, RAG, embeddings, vector databases APIs and Services REST APIs, FastAPI, Flask, JSON, microservices, event-driven integration Cloud and Data AWS, Azure or Google Cloud Platform; Snowflake, Databricks, Spark or PySpark DevOps and MLOps ## Description We are seeking a highly skilled Senior Consultant - Python AI/ML Engineer to design, build, and deploy scalable enterprise AI solutions. The ideal candidate combines strong Python engineering skills with hands-on experience in machine learning, Generative AI, cloud platforms, API development, MLOps, and CI/CD automation. The role partners with business stakeholders, architects, data engineers, product teams, and DevOps teams to deliver secure, reliable, and production-ready AI-enabled applications., AI/ML and Generative AI Development * Design, develop, test, and deploy machine learning and Generative AI solutions for enterprise use cases. * Build and optimize end-to-end AI/ML pipelines covering data ingestion, feature engineering, model training, evaluation, deployment, and monitoring. * Develop Retrieval-Augmented Generation (RAG) solutions and integrate large language models into enterprise applications. * Perform model evaluation, performance tuning, prompt optimization, and ongoing solution improvement. Python and API Engineering * Develop scalable backend services, microservices, and RESTful APIs using Python. * Create reusable frameworks, libraries, and integration components. * Write clean, secure, maintainable, and production-quality code using modern engineering standards. * Implement unit, integration, regression, and performance testing. Data Engineering and Integration * Design data pipelines for structured and unstructured data processing. * Integrate AI/ML models with internal and external enterprise systems through APIs and event-driven patterns. * Work with cloud data platforms such as Snowflake, Databricks, or comparable technologies. * Collaborate with data engineering teams to support data quality, lineage, governance, and security requirements. MLOps, DevOps and CI/CD * Build and maintain CI/CD pipelines for application, infrastructure, and model deployments. * Automate build, test, code-quality checks, security scanning, deployment, and release processes. * Implement MLOps practices including model versioning, experiment tracking, monitoring, drift detection, and retraining. * Use tools such as Jenkins, GitHub Actions, Azure DevOps, Bitbucket Pipelines, Docker, and Kubernetes. Cloud Engineering * Design and deploy cloud-native solutions using AWS, Azure, or Google Cloud Platform. * Use containers, orchestration platforms, and managed cloud services to deliver scalable and resilient solutions. * Apply enterprise security, observability, access-control, and high-availability standards. Collaboration and Technical Leadership * Translate business requirements into practical technical designs and implementation plans. * Partner with product owners, architects, data engineers, DevOps teams, and business stakeholders. * Lead design reviews, code reviews, troubleshooting, and technology evaluations. * Mentor engineers, promote engineering best practices, and contribute to Agile delivery ceremonies., Jenkins, GitHub Actions, Azure DevOps, Bitbucket Pipelines, Git, Docker, Kubernetes, model monitoring Success Measures * Deliver scalable, secure, and supportable AI/ML solutions into production. * Establish reliable CI/CD and MLOps practices that improve deployment quality and repeatability. * Create measurable business value through AI-enabled applications, automation, and data-driven capabilities. * Raise engineering standards through technical leadership, mentoring, documentation, and reusable solution patterns.