AI/ML Solution Engineer
- Collaborate with clients to understand business needs and translate them into AI/ML-driven solutions.
- Build, fine-tune, and integrate AI/ML models (LLMs, NLP, computer vision, deep learning, traditional ML).
- Deliver applications that are reliable, intuitive, and built for long-term value.
- Navigate emerging and rapidly changing AI/ML technologies and tools.
- Adapt quickly, lead through change, and make confident decisions with limited information.
- Push boundaries by recommending modern approaches and innovations in AI/ML.
- Ask the right questions to uncover root causes and deliver solutions that solve the real problem.
- Dig into data pipelines, model architectures, optimization techniques, and workflows that drive better outcomes.
- Explore new capabilities in deep learning frameworks, MLOps practices, and cloud platforms.
- Architect scalable, maintainable AI/ML solutions that go beyond one-off experiments.
- Uphold security, data governance, and best practices in every deployment.
- Monitor model performance, retrain when necessary, and implement CI/CD pipelines for continuous improvement.
- Coach and guide other team members to elevate technical delivery and AI/ML literacy.
- Be scrappy, stay hungry. Move fast, experiment, and adapt.
- Roll up your sleeves and get your hands dirty with coding, data processing, deployment, and optimization.
- Push through challenges with grit and a growth mindset.
- Work collaboratively across teams, combining diverse skills and perspectives to drive better outcomes.
- Translate technical AI/ML concepts into clear insights for non-technical stakeholders.
- Communicate complex data, models, and outcomes in a way that’s clear and actionable.
- Proficiency in Python (required); Java, JavaScript, SQL/NoSQL, and data processing tools (NumPy, Pandas, PySpark). Understanding of C++, R, Julia, Scala a plus.
- Proven experience building and deploying LLM applications, developing agentic workflows, and applying advanced prompting and fine-tuning strategies.
- Knowledge of ETL/ELT pipelines, familiarity with big data frameworks, data pipeline tools, SQL/NoSQL
- Hands-on experience with model development, training, deployment, and maintenance. Experinece with Scikit-learn, TensorFlow, PyTorch, XGBoost, Hugging Face, and modern architectures (CNNs, RNNs, Transformers).
- Experience with CI/CD pipelines, experiment tracking, model monitoring, versioning, and building scalable ETL/data pipelines.
- Experience with AWS, Azure, GCP cloud infrastructures. Containerization and Automation (Docker, Kubernetes, Jenkins, GitHub Actions).
- Solid foundations in linear algebra, calculus, probability/statistics; familiarity with responsible AI practices, ethics, bias, and governance a plus.
- Excellent communication skills to engage both technical and non-technical stakeholders, with a collaborative, problem-solving mindset.
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