Finch Computing Solutions Engineer
Finch Computing is a fast-growing, fast-paced software development organization; our mission is to build new ways of interacting with information. We do that by leveraging game-changing intellectual property, cloud infrastructure expertise, and a staff that is second to none. Together, we build and support products that address complex, real-time data and analytics needs in the enterprise.
We're looking for a client focused ML Solutions Engineer with an entrepreneurial mindset to help build out our Solutions Engineering team. You’ll be the trusted technical advisors for our customers, driving business value, offering advice, and growing accounts. You’ll accomplish this by leading customers to solutions oftentimes by teaching the product to new users or consulting on best practices. You must be ready for technical discussions with data scientists and engineers, then demonstrate our value in business discussions with directors and executives. The goal is to enable our customers to become successful and enthusiastic about Finch Computing.
Clearance Level: Top Secret
What You’ll Do
- You will act as a trusted advisor to our customers, while also building relationships with technical stakeholders
- Regularly engage with clients for both virtual and on-site status calls, educating on product roadmap and QBRs, managing escalations, while influencing our roadmap in partnership with our Product team.
- Interface with our pre-sales engineering team to gather client goals and KPI’s.
- Spearhead new opportunities in which Finch Computing can provide the most value that will drive renewals and new accounts.
What We’re Looking For
- Comfortable with Kubernetes, Docker and public Cloud environments (AWS, Azure, GCP).
- Data integration experience to extend our capabilities with customer data needs.
- Prior experience with AI/ML integrations.
- Proficiency in a programming language (Python, R, Go, Java, etc)
Preferred
- Understanding of ML/DS concepts, model evaluation strategies and lifecycle (feature generation, model training, model deployment, batch and real time scoring via REST APIs) and engineering considerations
- Knowledge of machine learning frameworks such as TensorFlow, PyTorch or Scikit-learn.
- Excellent communication and interpersonal skills - Ability to simplify complex, technical concepts.
- A quick and self-learner - undaunted by technical complexity of production ML deployments and welcome the challenge to learn about them and develop your own point of view.
Nice To Have
- Previous experience as a Data Scientist, Machine Learning Engineer or ML Ops/Production.
Location: Hybrid. Must be based in the Washington, DC metro area. Local travel within DC/MD/VA area and to Richmond, VA.