Principal AI/ML Software Engineer

**Responsibilities** Position Overview We are seeking a motivated AI/ML Engineer to build reliable, scalable systems and Generative AI and Agentic AI features, and build and deploy data-driven solutions for our document-based compliance management platform. This role requires a technical expert who can develop, deploy, and maintain ML systems in production environments.

**Key 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

LI-Remote

**Education / Qualifications** Technical 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

**Experience & Education**

• 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

**Preferred Qualifications**

• 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

Key Competencies

• Strong communication skills explaining complex models to technical and nontechnical audiences
• Ability to work independently and collaboratively in fast-paced environments
• Proven ability to convert POCs into production-grade solutions
• Understanding of ethical AI and building trustworthy, explainable systems for regulated environments

**What You’ll Build**

• LLM evaluation frameworks ensuring 95%+ accuracy for compliance-critical features
• Prompts for LLMs to achieve specific, high-quality outcomes
• Agentic AI systems autonomously handling document review and compliance workflows
• GenAI document understanding features processing millions of regulatory documents
• Predictive models identifying compliance risks before they occur
• Real-time semantic search and explainable ML systems meeting regulatory requirements
• Production MLOps pipelines supporting dozens of models with automated monitoring and retrain…