Gen AI Lead

Synechron•New York, NY

About The Position

Synechron is seeking an experienced GenAI Lead to drive the design, development, and implementation of Generative AI solutions across the organization. The role leads technical strategy and engineering teams while partnering with business stakeholders to deliver secure, scalable, production-ready AI applications. Areas of focus include large language models, retrieval-augmented generation, AI agents, prompt engineering, model evaluation, and responsible AI. The position combines technical expertise, leadership, and business understanding to turn emerging AI capabilities into measurable business value. The GenAI Lead defines the technical roadmap, leads solution architecture and delivery, and develops reusable frameworks and components for enterprise adoption. The role oversees integration with enterprise applications and data platforms, establishes quality and operational standards, and promotes responsible AI practices. It also includes mentoring engineering teams, presenting strategies and outcomes to senior leadership, and contributing to pre-sales and client engagements when required. For a position filled in New York, NY, the stated base salary is $140,000–$155,000 per year, plus benefits. Salary varies by geography and other factors.

Requirements

12 or more years of overall experience in software engineering, data science, machine learning, or AI solution delivery.
At least 2 years of hands-on experience designing and implementing Generative AI solutions.
Strong experience with large language models and foundation model platforms such as OpenAI, Azure OpenAI, Amazon Bedrock, Google Vertex AI, Anthropic, or open-source models.
Proven experience building and deploying RAG-based applications and AI assistants.
Strong Python programming skills and familiarity with software development best practices.
Experience with AI/ML frameworks and libraries such as LangChain, LlamaIndex, Hugging Face, PyTorch, TensorFlow, or similar technologies.
Experience with vector databases such as Pinecone, FAISS, Weaviate, Milvus, Chroma, or Azure AI Search.
Strong understanding of APIs, microservices, cloud platforms, databases, and enterprise application integration.
Experience with model evaluation, prompt optimization, hallucination mitigation, and AI application testing.
Knowledge of MLOps, LLMOps, CI/CD, containerization, and deployment automation.
Experience leading technical teams, projects, or enterprise AI initiatives.
Strong communication, stakeholder management, analytical, and problem-solving skills.

Nice To Haves

Experience developing autonomous or semi-autonomous AI agents and multi-agent systems.
Experience with multimodal AI, including text, image, audio, or document intelligence.
Experience with model fine-tuning, parameter-efficient fine-tuning, and synthetic data generation.
Knowledge of knowledge graphs, graph databases, or semantic search.
Experience implementing AI governance, security, privacy, and compliance controls.
Familiarity with cloud services from AWS, Microsoft Azure, or Google Cloud.
Experience in regulated industries such as financial services, healthcare, insurance, or government.
Experience with enterprise security concepts, data masking, access controls, and responsible AI frameworks.
Relevant certifications in cloud, AI, machine learning, or data engineering.
Experience publishing technical content, presenting at conferences, or contributing to open-source projects.

Responsibilities

Define and execute the organization’s Generative AI technical strategy and roadmap.
Lead the architecture, design, development, and deployment of GenAI solutions.
Build production-grade applications using large language models, foundation models, and AI services.
Design and implement RAG, AI agents and agentic workflows, prompt engineering and orchestration, fine-tuning and model adaptation, embeddings and vector databases, and text generation, summarization, classification, extraction, and conversational AI solutions.
Evaluate and select commercial, open-source, and cloud-based AI models.
Develop reusable frameworks, accelerators, APIs, and components for GenAI adoption.
Lead integration of GenAI solutions with enterprise applications, data platforms, APIs, and business processes.
Establish standards for model evaluation, accuracy, reliability, performance, cost, security, and scalability.
Implement observability, monitoring, feedback loops, and continuous improvement processes for AI applications.
Ensure responsible AI practices, including privacy, security, fairness, explainability, governance, and regulatory compliance.
Collaborate with data scientists, software engineers, cloud architects, product managers, cybersecurity teams, and business stakeholders.
Translate business requirements into practical AI use cases and technical solutions.
Lead proofs of concept and transition validated solutions into production.
Mentor and guide engineering teams in GenAI architecture, development, testing, and deployment.
Stay current with advancements in LLMs, multimodal AI, AI agents, model tooling, and industry best practices.
Present technical strategies, solution designs, prototypes, and business outcomes to senior leadership.
Contribute to pre-sales activities, proposals, technical demonstrations, and client engagements when required.

Benefits

Competitive compensation and benefits package.
Possibility to work abroad.
10 days of paid annual leave, plus sick leave and national holidays.
Maternity and paternity leave plans.
Comprehensive insurance including medical, dental, vision, life insurance, and long- and short-term disability; plans vary by region.
Retirement savings plans.
Higher education certification policy.
Commuter benefits, which vary by region.
Extensive training focused on skills, substantive knowledge, and personal development.
On-demand Udemy for Business access to more than 5,000 curated courses.
Coaching opportunities with colleagues from Financial Innovation Labs (FinLabs) and Center of Excellences (CoE) groups.
Flexible workplace arrangements, mentoring, internal mobility, and learning and development programs.