Note: The job is a remote job and is open to candidates in USA. Octave provides mission-critical software that empowers organizations to make informed decisions across every stage of the asset lifecycle. They are seeking a Principal AI/ML Software Engineer to build reliable, scalable systems and Generative AI features, as well as deploy data-driven solutions for their compliance management platform.
Responsibilities
- Build and deploy Generative AI features using foundation models (AWS Bedrock, OpenAI, Anthropic Claude) and inference pipelines with optimization of latency and cost
- Design agentic AI systems that autonomously handle compliance workflows, document review, regulatory mapping, and multi-step reasoning tasks
- Integrate comprehensive LLM evaluation frameworks with development and production systems
- Build and operate end-to-end MLOps pipelines, deployment systems, monitoring, and rollbacks workflows
- Implement explainability frameworks (SHAP/LIME) and monitoring dashboards ensuring transparency and regulatory adherence
- Collaborate with cross-functional teams to translate business needs into ML solutions and communicate insights to stakeholders
Skills
- Python (5+ years): Production-level experience with Pandas, NumPy, scikit-learn, XGBoost, TensorFlow/PyTorch, Hugging Face Transformers, FastAPI/Flask, MLflow, and pytest
- SQL: Advanced proficiency with complex queries, window functions, and optimization
- Machine Learning & NLP: Strong foundation in supervised/unsupervised learning, deep learning, document understanding, text classification, and semantic analysis
- Generative AI & LLMs: Hands-on experience with foundation models (GPT, Claude, Llama), prompt engineering, RAG architectures, and vector databases (Pinecone, Weaviate, Chroma)
- MLOps & ModelOps: End-to-end experience with ML pipelines, model versioning, feature stores, drift detection, CI/CD for ML, and Docker containerization
- LLM Evaluation: Experience with evaluation frameworks (RAGAS, DeepEval), custom metrics, benchmark datasets, and human-in-the-loop validation
- Cloud & AWS: Experience with AWS services including SageMaker, Bedrock, S3, Lambda, EC2, and CloudWatch
- Statistics & Experimentation: Strong foundation in statistics, A/B testing, causal inference, and experimental design
- Visualization: Proficiency with Tableau, Power BI, or Python visualization libraries
- 5+ years in data science, ML engineering, or related roles
- 3+ years building NLP/generative AI applications and implementing MLOps in production
- Bachelor's or Master's degree in Data Science, Computer Science, Statistics, or related field
- Track record of deploying ML systems processing large-scale datasets with proper monitoring and governance
- Experience with agentic AI frameworks (LangGraph, LangChain, AutoGen, CrewAI)
- Knowledge of Life Sciences/regulated industries (FDA, EMA, ISO, GxP) and compliance management systems
- Familiarity with big data tools (Spark, Databricks, Snowflake), orchestration (Airflow, Kubeflow), and monitoring tools (Datadog, Prometheus)
- Experience with LLM fine-tuning, document processing libraries, multi-modal AI, or distributed training
- Understanding of ML governance, bias detection, model risk management, and data privacy regulations (GDPR, CCPA, HIPAA)
- Experience working in agile environments with Jira
- AWS ML certifications or similar credentials
Benefits
- Workplace Type: Remote
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