About the Role
We’re looking for an AI/ML Software Engineer with hands-on experience across traditional machine learning and Generative AI / LLM systems. This role is for a builder who can take models from concept to production—combining data, ML algorithms, and LLM workflows to deliver reliable, scalable AI features.
Key Responsibilities
- Build, deploy, and maintain production ML and Generative AI systems
- Develop end-to-end ML pipelines (data ingestion, feature engineering, training, evaluation, deployment)
- Design and operate LLM workflows (prompting, RAG, tools, agents)
- Implement supervised and unsupervised ML models (classification, regression, clustering, anomaly detection)
- Perform feature engineering, model tuning, and validation
- Integrate models into APIs and microservicesMonitor model performance, drift, accuracy, latency, and cost
- Collaborate with data, platform, and product teams
Required Qualifications
- 3–5 years of experience in AI/ML or Software Engineering
- Strong Python development skills
- Solid understanding of machine learning fundamentals: Classification, regression, clustering. Feature engineering and model evaluation
- Hands-on experience with Generative AI / LLMs
- Experience with ML frameworks (Scikit-learn, PyTorch, TensorFlow)
- Experience deploying models into production environments
- Familiarity with REST APIs and service-based architectures
- Strong SQL skills and experience working with structured data
Preferred / Nice to Have
- Experience with time-series modeling, forecasting, or anomaly detection
- Experience with RAG architectures and vector databases
- Familiarity with LangChain, LlamaIndex, or similar LLM frameworks
- Experience with embeddings and semantic search
- Exposure to LLM fine-tuning (LoRA, PEFT, QLoRA)
- MLOps experience (MLflow, Kubeflow, Airflow)
- Docker, Kubernetes, and cloud platforms (AWS, Azure, GCP)
- Experience with streaming or large-scale data systems (Kafka, Spark)
What We’re Looking For
- Strong engineering fundamentals with an ML mindset
- Experience shipping ML + GenAI features to production
- Ability to evaluate tradeoffs between accuracy, latency, and cost
- Comfortable working across data, infrastructure, and product teams