AI/ML with Python & AWS - Generative AI, LangChain, Bedrock, Lambda, API Gateway

Requirements

• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or related fields.
• 7+ years of experience in AI/ML development with Python, supporting enterprise applications.
• Demonstrable expertise with Amazon AWS, especially Bedrock, Lambda, and API Gateway.
• Strong knowledge of prompt engineering,

Benefits

Additional Information

Job Summary Synechron is seeking a skilled AI/ML Developer specializing in Python and AWS to lead the development of innovative Generative AI and Large Language Model (LLM) solutions. In this role, you will design and implement end-to-end AI applications utilizing AWS Bedrock, Lambda, API Gateway, and orchestration frameworks like LangChain. The focus will be on building scalable, serverless AI systems, integrating LLMs with enterprise environments, and advancing AI agent architectures, all aimed at transforming business processes and enabling intelligent automation. Software Requirements Required: Strong expertise in Python development for AI/ML applications, including prompt engineering and RAG pipelines. Hands-on experience with AWS services: AWS Bedrock, Lambda, API Gateway . Practical experience with AI frameworks such as LangChain and Agent Core or equivalents. Familiarity with version control tools like Git . Knowledge of data management and integration techniques relevant to AI workflows. Preferred: Exposure to additional AI frameworks such as PyTorch or TensorFlow . Experience working with vector databases (FAISS, Pinecone, Weaviate). Knowledge of Model Context Protocol (MCP) and research automation stacks like QuickSuite or Deep Research . Experience with containerization and orchestration tools such as Kubernetes . Overall Responsibilities Design and implement comprehensive GenAI and LLM-based solutions leveraging Python and AWS cloud infrastructure. Build intelligent, scalable pipelines using AWS Bedrock, foundation models, and prompt-engineering techniques. Develop and deploy AI agents capable of multi-step reasoning, tool calling, and workflow orchestration. Create serverless AI applications utilizing AWS Lambda, API Gateway, and event-driven architectures. Integrate LLMs with enterprise systems, APIs, vector databases, and retrieval-augmented generation (RAG) solutions. Optimize AI models and pipelines for performance, reliability, and scalability in cloud environments. Lead the development of multi-agent systems, hierarchical agents, and automation stacks. Collaborate with cross-functional teams to define technical requirements, validate AI solutions, and support deployment. Monitor, evaluate, and improve the performance and accuracy of AI systems through testing and iteration. Expected Outcomes: Deliver robust, efficient, and scalable AI solutions that enable enterprise automation, enhance decision-making, and foster innovation. Technical Skills (By Category) Programming Languages: Essential: Python, prompt engineering, relevant scripting skills Preferred: Java, JavaScript, or other automation scripting languages AI Frameworks & Libraries: Essential: LangChain, Agent Core, or similar agent frameworks Preferred: PyTorch, TensorFlow, Hugging Face Transformers Cloud Technologies: Essential: AWS Bedrock, Lambda, API Gateway Preferred: Additional AWS services (Fargate, EKS), Azure, GCP Data & Storage: Basic understanding of vector databases (FAISS, Pinecone, Weaviate). Development & Automation Tools: Essential: Git, CI/CD pipelines (GitHub Actions, Jenkins), Docker (preferred) Preferred: Terraform, CloudFormation, DataOps tools Security & Compliance: Knowledge of data privacy, security best practices, and compliance frameworks related to AI deployment. Experience Requirements Minimum 7+ years of professional experience developing AI/ML solutions with Python. Hands-on experience architecting and deploying Generative AI and LLM solutions in production environments. Proven experience building intelligent pipelines with AWS Bedrock, Lambda, and API Gateway. Expertise in integrating AI systems with enterprise APIs, databases, and vector stores. Experience leading automation efforts, multi-agent solutions, and research-based automation stacks. Industry exposure to finance, healthcare, or enterprise automation is advantageous; alternative sectors are acceptable. Day-to-Day Activities Develop, test, and deploy AI/ML models and pipelines integrating foundation models within AWS cloud architectures. Build and orchestrate multi-step reasoning workflows with LangChain and Agent Core. Optimize models and code for scalability and efficiency, troubleshooting issues promptly. Collaborate with data scientists, product owners, and enterprise teams to define system requirements. Monitor system performance, update models, and refine prompts for accuracy improvements. Support deployment activities, including automation, validation, and documentation of solutions. Participate in technical discussions, code reviews, and knowledge sharing sessions.

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