LangChain, Semantic Kernel or Similar Frameworks: Practical experience building LLM applications, orchestration workflows or AI agents.
APIs and Microservices: Experience designing or integrating APIs and microservices for AI-enabled applications.
Docker and Kubernetes: Experience containerizing, deploying and managing AI/ML applications.
CI/CD Pipelines: Experience integrating software and model delivery into automated build, test and deployment pipelines.
SQL and NoSQL Databases: Experience working with structured and unstructured data stores.
Large-Scale Data Processing: Experience developing or supporting data pipelines for AI/ML solutions.
MLOps: Experience with model deployment, monitoring, versioning, reliability and operational support.
Preferred
Experience with AI governance, responsible AI practices and model monitoring.
Experience leading technical teams or AI/ML projects.
Certification in Azure AI, AWS Machine Learning or equivalent cloud technologies.
Experience with enterprise-scale Generative AI platforms and production LLM applications.
Experience with model optimization, evaluation frameworks, observability and cost management.
Experience implementing reusable AI platforms and shared services.
Overall Responsibilities
Lead the design, development and deployment of AI/ML and Generative AI solutions.
Architect scalable AI platforms that meet functional, performance, security, reliability and maintainability requirements.
Collaborate with business and technical teams to translate requirements into practical AI-driven solutions.
Develop, optimize and evaluate machine learning models, LLM-based applications, AI agents and data pipelines.
Design RAG solutions using embeddings, vector databases, retrieval strategies and prompt engineering.
Drive technical discussions, code reviews and solution architecture decisions.
Establish development standards, reusable components, coding practices and engineering controls for AI solutions.
Mentor and guide AI/ML engineers and developers through technical coaching, design reviews and delivery support.
Ensure model performance, reliability, security and scalability in production environments.
Support model deployment, monitoring, versioning, incident resolution and continuous improvement through MLOps practices.
Integrate AI capabilities with APIs, microservices, databases and enterprise applications.
Use Docker, Kubernetes and CI/CD pipelines to support repeatable and controlled delivery.
Assess emerging AI technologies and industry trends for their relevance to Synechron’s products and delivery objectives.
Support responsible AI, model governance, data protection, explainability and appropriate human oversight.
Manage technical risks, dependencies, delivery priorities and architectural trade-offs.
Promote sustainable AI engineering by considering compute efficiency, model utilization, reuse, infrastructure optimization and long-term maintainability.
Technical Skills (By Category)
Programming Languages
Essential
Strong proficiency in Python for AI/ML development, model implementation, data processing and automation.
Ability to write maintainable, testable and production-ready software.
Ability to develop supporting services, integrations and utilities for AI-enabled applications.
Preferred
Experience with additional programming languages used in microservices, APIs or enterprise application integration.
Experience developing asynchronous, distributed or high-throughput AI services.
Databases and Data Management
Essential
Experience working with SQL and NoSQL databases.
Experience designing and supporting data pipelines for large-scale data processing.
Understanding of data preparation, data quality, feature engineering, data access and data lineage.
Experience with vector databases and embedding-based retrieval.
Ability to manage structured, unstructured and semi-structured data used by AI/ML applications.
Preferred
Experience with data lake, warehouse or distributed data-processing architectures.
Experience with data governance, metadata management and data-quality monitoring.
Experience optimizing vector search, indexing and retrieval performance.
Cloud Technologies
Essential
Hands-on experience with at least one of the following:Azure AI or Azure OpenAIAWS AI/ML servicesGoogle Cloud AI services
Ability to design, deploy and operate AI/ML workloads in cloud environments.
Understanding of cloud scalability, availability, monitoring, access control and cost considerations.
Preferred
Experience designing multi-service or multi-environment AI platforms.
Experience with cloud-based model deployment, managed AI services and infrastructure automation.
Exposure to cloud cost optimization for large-scale model usage and data processing.
Frameworks and Libraries
Essential
TensorFlow, PyTorch and/or Scikit-learn for machine learning and deep learning development.
LangChain, Semantic Kernel or a similar framework for LLM application and AI-agent development.
Experience with Generative AI, LLMs, RAG and prompt engineering.
Experience building and integrating APIs and microservices.
Understanding of NLP concepts and deep learning techniques.
Preferred
Experience with LLM evaluation, fine-tuning, grounding, guardrails and response-quality measurement.
Experience developing reusable orchestration components and AI-agent workflows.
Familiarity with model-serving frameworks and AI application observability tools.
Development Tools and Methodologies
Essential
Docker for containerization of AI/ML applications and services.
Kubernetes for deployment and management of containerized workloads.
Git-based software development and source control practices.
CI/CD pipelines for automated build, test, deployment and release management.
MLOps practices covering model deployment, versioning, monitoring and operational support.
Code reviews, technical design reviews, automated testing and software engineering best practices.
Agile delivery and collaborative development methods.
Preferred
Experience leading technical delivery across multiple AI/ML workstreams.
Experience with infrastructure-as-code, automated environment provisioning and release governance.
Experience implementing monitoring for model performance, data drift, latency, availability and usage.
Security Protocols
Essential
Understanding of secure AI application design, API security, access management and protection of sensitive data.
Awareness of privacy, data handling, authentication, authorization and secure deployment requirements.
Ability to assess security risks associated with LLMs, RAG solutions, AI agents, prompts and retrieved content.
Understanding of responsible AI principles, model governance and appropriate human oversight.
Preferred
Experience implementing AI governance controls, model-risk management and auditability.
Experience with controls for prompt injection, data leakage, unauthorized model access and unsafe AI outputs.
Familiarity with security monitoring and compliance requirements for cloud-hosted AI workloads.
Experience Requirements
6–8 years of experience in software development, artificial intelligence, machine learning or a closely related technical field.
Strong hands-on experience developing AI/ML solutions using Python and frameworks such as TensorFlow, PyTorch or Scikit-learn.
Experience with Generative AI, LLMs, RAG, prompt engineering and AI agents.
Experience deploying or operating AI/ML solutions using Azure AI, Azure OpenAI, AWS AI/ML or Google Cloud AI services.
Experience with NLP, deep learning, model deployment and MLOps.
Experience with vector databases, LangChain, Semantic Kernel or similar frameworks.
Experience with APIs, microservices, Docker, Kubernetes and CI/CD pipelines.
Experience working with SQL and NoSQL databases and large-scale data-processing solutions.
Experience leading technical teams, projects or solution delivery workstreams.
Exposure to AI governance, responsible AI and model monitoring.
Experience delivering enterprise AI solutions in financial services, technology, consulting or another regulated or data-intensive industry.
Candidates may qualify through equivalent practical experience in software engineering, data engineering, machine learning engineering, platform engineering or Generative AI development that demonstrates the required capabilities, even where their previous job title was different.
Day-to-Day Activities
Design, develop and review AI/ML, Generative AI, LLM, RAG and AI-agent solutions, including supporting models, APIs, data pipelines and deployment components.
Collaborate with business stakeholders, architects, engineers and delivery teams to clarify requirements, assess technical options and agree solution designs.
Conduct code reviews, technical discussions, model-performance reviews and production-readiness assessments while mentoring AI/ML engineers and developers.
Track technical risks, make architecture and implementation recommendations within the agreed governance framework, and support reliable, secure and scalable delivery.
Qualifications
A bachelor’s or master’s degree in Computer Science, Artificial Intelligence, Data Science or a related field is preferred; equivalent relevant professional experience may be considered.
Certification in Azure AI, AWS Machine Learning or an equivalent cloud technology is preferred.
Practical training or demonstrated experience in machine learning, Generative AI, LLMs, RAG, prompt engineering, AI agents and MLOps is required.
Training in secure software engineering, responsible AI, cloud architecture, model monitoring and data protection is preferred.
Commitment to continuous professional development in emerging AI technologies, cloud services, model engineering, governance and production AI practices is expected.
Professional Competencies
Applies critical thinking to evaluate business problems, select appropriate AI approaches, assess model limitations and resolve technical issues.
Leads through clear technical direction, constructive feedback, mentoring, code reviews and effective collaboration across engineering and delivery teams.
Communicates architecture decisions, technical risks, model behavior, delivery progress and business impacts clearly to technical and non-technical stakeholders.
Adapts to evolving AI technologies, cloud capabilities, frameworks, regulatory expectations and project requirements through continuous learning.
Identifies practical opportunities to use Generative AI, automation, reusable platforms and improved engineering practices to deliver measurable business value.
Manages time, technical priorities, dependencies and delivery commitments effectively while maintaining appropriate standards for quality, security, scalability and reliability.
SYNECHRON’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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