Python Gen AI

Key Responsibilities

Work with business and technology stakeholders to understand Generative AI use cases and

translate requirements into practical implementations.

Develop proofs of concept, prototypes, and reference applications that demonstrate business

value and accelerate AI adoption.

Build and enhance AI-powered applications, copilots, assistants, and agentic workflows.

Implement Retrieval-Augmented Generation (RAG) solutions using enterprise knowledge

repositories, vector databases, embeddings, semantic search, and retrieval techniques.

Develop AI agents and orchestration workflows using frameworks such as LangGraph,

LangChain, Semantic Kernel, Claude Code SDK, OpenAI SDK, or similar technologies.

Implement prompt engineering techniques, structured outputs, tool calling, context

management, and workflow orchestration patterns.

Build APIs, connectors, and integrations between AI applications and enterprise systems, data

platforms, and business workflows.

Create reusable components, templates, implementation patterns, and developer examples that

help application teams deliver AI solutions more efficiently.

Develop automated tests and evaluation capabilities to measure solution quality, grounding,

reliability, safety, and performance.

Troubleshoot implementation issues and support application teams as prototypes and reference

solutions transition toward production.

Collaborate with firmwide AI platform teams to use foundational AI capabilities and follow

enterprise engineering, security, governance, and Responsible AI standards.

Stay current with emerging AI technologies and apply relevant tools and techniques to business

use cases.

Role Overview and Expectations

Applied AI engineers are hands-on developers who work collaboratively with business and technology

teams to accelerate the implementation of Generative AI use cases

Team members participate in discovery discussions, build and test prototypes, integrate AI capabilities

with existing applications, and provide practical implementation support to sponsoring application

development teams.

Success in this role requires solid software engineering fundamentals, practical experience with AI

application development, strong problem-solving skills, and the ability to communicate clearly and work

effectively across teams.

Required Qualifications

3 to 5 years of software engineering experience with hands-on application development

responsibilities.

1 to 3 years of practical experience developing Generative AI applications, AI assistants, AI

agents, copilots, or intelligent automation solutions.

Proficiency in Python and experience building APIs, integrations, and enterprise applications.

Working knowledge of Large Language Models (LLMs), prompt engineering, embeddings, vector

databases, semantic search, Retrieval-Augmented Generation (RAG), and common AI

application patterns.

Hands-on experience with one or more AI development frameworks, such as LangGraph,

LangChain, , Claude Code SDK, OpenAI SDK, or similar technologies.

Familiarity with Model Context Protocol (MCP), AI gateways, agent-to-agent communication, or

tool-calling frameworks.

Experience implementing AI agents, orchestration workflows, tool integrations, or agentic

applications.

Experience building proofs of concept, prototypes, or reference implementations.

Experience integrating applications with enterprise systems, data platforms, and APIs.

Familiarity with automated testing and evaluation approaches for AI solution quality, safety,

reliability, and performance.

Awareness of Responsible AI principles, AI governance, security controls, and enterprise data

protection requirements.

Experience with GitHub, CI/CD pipelines, containers, and modern software engineering

practices.

Familiarity with Azure or AWS services used to host and operate applications and AI workloads.

Ability to work with business stakeholders and application development teams to translate

requirements into working software.

Strong communication, collaboration, and problem-solving skills.

Preferred Qualifications

Experience with Azure AI Foundry, AWS Bedrock, Anthropic Claude, OpenAI GPT models, SpaceX

Grok Models, Gemini, or similar enterprise AI platforms.

Experience building RAG solutions, knowledge assistants, or AI-powered search capabilities.

Exposure to multi-agent systems and agent orchestration workflows.

Experience integrating applications with ServiceNow, Jira, Confluence, SharePoint, Microsoft

Graph, Databricks, Snowflake, or similar enterprise platforms.

Familiarity with Kubernetes, containerized application deployment, and cloud-native

architectures.

Experience working in financial services or another regulated industry.

S​ YNECHRON’S DIVERSITY & INCLUSION STATEMENT

Diversity & Inclusion are fundamental to our culture, and Synechron is proud to be an equal opportunity workplace and is an affirmative action employer. Our Diversity, Equity, and Inclusion (DEI) initiative ‘Same Difference’ is committed to fostering an inclusive culture – promoting equality, diversity and an environment that is respectful to all. We strongly believe that a diverse workforce helps build stronger, successful businesses as a global company. We encourage applicants from across diverse backgrounds, race, ethnicities, religion, age, marital status, gender, sexual orientations, or disabilities to apply. We empower our global workforce by offering flexible workplace arrangements, mentoring, internal mobility, learning and development programs, and more.

All employment decisions at Synechron are based on business needs, job requirements and individual qualifications, without regard to the applicant’s gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law .

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