Role overview
Solution Street, a software engineering firm, was founded by a software developer who envisioned a safe haven for software engineers who wanted to work on interesting, fun projects. Since 2002, we’ve stuck by this principle and as a result, we’ve developed long, lasting relationships with our clients and have a team of great developers who love what they do.
We enjoy working with cutting edge technologies and providing solutions to complex business problems. Our employees are experts in building large, highly scalable and well performing web applications using many technologies. We are Microsoft and AWS partners.
At Solution Street we value all employees and job candidates as unique individuals, and we welcome the variety of experiences they bring to our organization. As such, we have a strict non-discrimination policy. We believe everyone should be treated equally regardless of race, sex, gender identification, sexual orientation, national origin, native language, religion, age, disability, marital status, citizenship, genetic information, pregnancy, or any other characteristic protected by law.
What you'll work on
- Design, develop, and deploy machine learning and AI models for production systems.
- Build end-to-end AI pipelines, including data ingestion, training, evaluation, and inference.
- Optimize models for performance, scalability, reliability, and cost.
- Work with large datasets and ensure data quality, versioning, and governance.
- Integrate AI/ML solutions into existing backend and frontend systems.
- Implement MLOps best practices (CI/CD, monitoring, retraining, model versioning).
- Evaluate and experiment with LLMs, generative AI, and classical ML techniques.
- Ensure AI systems meet security, privacy, and ethical standards.
- Mentor junior engineers and contribute to architectural decisions.
- Stay current with advances in AI research, tooling, and industry trends.
What we're looking for
- Experience with LLMs, prompt engineering, RAG, or fine-tuning models.
- Knowledge of MLOps tools (MLflow, Kubeflow, Airflow, SageMaker, Vertex AI).
- Experience with distributed systems and large-scale data processing (Spark, Kafka).
- Exposure to vector databases and embedding-based search.
- Background in applied AI domains such as NLP, computer vision, or recommender systems.
- Experience mentoring engineers or leading technical initiatives.