Build, deploy, and operationalize AI-powered applications that integrate LLMs with structured enterprise data. Design production-grade infrastructure, model hosting, and data pipelines, collaborate with product and engineering, and work directly with clients to translate workflows into scalable AI solutions. Implement monitoring, reliability, and secure deployments for life sciences use cases.
We’re building the next generation of conversational voice and video AI for life sciences—intelligent with 100% regulatory compliance. Our AI medical experts help pharma teams engage doctors, support patients, and unlock insights faster than ever before. We’ve just raised our seed, signed our first customers, and are scaling fast. If you're excited about building real-world AI in an early stage startup and shape not just our product, but our identity and culture —this is your moment.
We move like owners. Everyone at SynthioLabs has the autonomy and the responsibility to drive projects, challenge decisions, and push things forward. We don’t wait for permission. If you see a gap, you fill it. If you see a better way, you try it.
We ask “why” until we get to the root. We explore new ideas, research papers, every single edge case and anything that sharpens our understanding.
We use AI like a second brain. From writing code to writing docs, debugging flows to exploring new ideas, AI is part of our daily rhythm. Everyone here gets hands-on with the tools, learns how to prompt better, and treats AI as a teammate.
We are looking for a Forward Deployed Engineer – Tech to work at the intersection of AI engineering, product deployment, and client engagement. This role partners closely with customers and internal engineering teams to build, deploy, and operationalize AI-powered applications in real-world commercial environments.
You will combine software engineering, AI application development, and product thinking to translate complex business workflows into scalable AI systems. The role requires hands-on experience building AI-enabled applications, integrating LLMs, and deploying production-grade infrastructure, along with the ability to work directly with clients to implement solutions.
This position is ideal for engineers who enjoy building quickly, solving ambiguous problems, working directly with customers, and shipping production systems that power real-world AI applications.
- Build AI-powered applications using LLMs, APIs, and modern AI frameworks.
- Design and implement workflows that combine structured enterprise data with generative AI capabilities.
- Develop prototypes and production-ready systems that support real-world client deployments.
- Design and manage infrastructure required to deploy AI applications reliably and securely.
- Work with cloud environments to support model hosting, data pipelines, and scalable application deployment.
- Implement monitoring, performance optimization, and system reliability practices.
- Work directly with clients and internal teams to implement and customize AI-powered applications for commercial and operational use cases.
- Translate client workflows and requirements into scalable AI-driven software solutions.
- Serve as the technical bridge between client teams, product, and engineering.
- Partner with product and engineering teams to translate client needs into scalable platform capabilities.
- Contribute to the development of reusable infrastructure and components for AI application deployment.
- Support rapid prototyping and experimentation to accelerate product development.
Stakeholder Communication
- Work closely with client technical teams to integrate AI solutions into their environments.
- Explain technical architectures and deployment approaches to both technical and non-technical stakeholders.
- Collaborate cross-functionally across product, engineering, and founders directly.
Education
Bachelor’s or Master’s degree in Computer Science, Engineering, AI/ML, or a related technical field.
- 0–3 years of experience in software engineering, AI engineering, or machine learning systems development.
- Experience building applications using Python, JavaScript, or similar languages.
- Experience working with LLM APIs, AI frameworks, or generative AI tooling is preferred.
- Familiarity with cloud infrastructure and application deployment is preferred.
Technical Skills
- Experience building and deploying production software systems.
- Familiarity with AI application development frameworks, APIs, and model integration.
- Understanding of software architecture, system design, and backend services.
- Experience with containerization, APIs, or cloud platforms is a plus.
Domain Knowledge
- Exposure to pharmaceutical or healthcare technology environments is a plus but not required.
- Interest in applying AI systems to life sciences commercial and operational workflows is preferred.
Skills
- Strong engineering and problem-solving abilities.
- Ability to build and iterate quickly in ambiguous environments.
- Comfort working in client-facing technical roles.
- Strong collaboration across engineering, product, and customer teams.
What Makes This Role Unique
- Work directly with clients to deploy real AI systems into production environments.
- Build AI-powered applications rather than just models or prototypes.
- Blend software engineering, AI infrastructure, and customer-facing problem solving.
- Help build the next generation of AI-native platforms for life sciences.
Similar Jobs
Machine Learning • Payments • Security • Software • Financial Services
Lead business analysis for Digital Identity projects: gather and document system requirements, define capabilities, create system flows, manage backlogs, roadmap and releases, mentor junior analysts, coordinate stakeholders, and drive process improvement within Agile frameworks.
Top Skills:
ConfluenceDynatraceJIRAKanbanMS OfficePostmanSafeScrumServicenowSoapui
Fintech • Information Technology • Insurance • Financial Services • Big Data Analytics
Serve as a technical functional analyst for risk-related programs, gathering and influencing requirements, coordinating vendors and development teams, managing project plans, timelines, budgets, and test plans, and integrating cloud and AI solutions into GRC and risk-management systems. Produce leadership-level documentation and status reporting while collaborating with Architecture, Risk IT, and business partners to deliver technical solutions that improve risk capabilities.
Top Skills:
AIAzure DevopsCloudPower BIPowerPointPythonSQL
Information Technology • Insurance • Software
Serve as a trusted advisor leading complex enterprise SaaS implementations for MGA/insurance clients. Gather requirements, configure applications, execute data conversions, manage full project lifecycle, deliver training and UAT, liaise with product/engineering, mentor consultants, and ensure projects meet scope, budget, timeline, and quality targets.
Top Skills:
AgileBackend Development SystemsMS OfficePmbokPolicy Administration SystemsRatersSaaSVertafore
What you need to know about the NYC Tech Scene
As the undisputed financial capital of the world, New York City is an epicenter of startup funding activity. The city has a thriving fintech scene and is a major player in verticals ranging from AI to biotech, cybersecurity and digital media. It also has universities like NYU, Columbia and Cornell Tech attracting students and researchers from across the globe, providing the ecosystem with a constant influx of world-class talent. And its East Coast location and three international airports make it a perfect spot for European companies establishing a foothold in the United States.
Key Facts About NYC Tech
- Number of Tech Workers: 549,200; 6% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Capgemini, Bloomberg, IBM, Spotify
- Key Industries: Artificial intelligence, Fintech
- Funding Landscape: $25.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Greycroft, Thrive Capital, Union Square Ventures, FirstMark Capital, Tiger Global Management, Tribeca Venture Partners, Insight Partners, Two Sigma Ventures
- Research Centers and Universities: Columbia University, New York University, Fordham University, CUNY, AI Now Institute, Flatiron Institute, C.N. Yang Institute for Theoretical Physics, NASA Space Radiation Laboratory
