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Calsoft
AI/ML Software Engineer
Calsoft
Pune•On-site•1-3 Yrs•2 hours ago
Job description
Overview
AI/ML Software Engineer
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
- Design, develop, and train predictive models for compliance risk scoring, regulatory change impact, anomaly detection, and time-series forecasting
- Write production-quality Python code for data processing, feature engineering, API development (FastAPI/Flask), and ETL/ELT workflows
- Implement explainability frameworks (SHAP/LIME) and monitoring dashboards ensuring transparency and regulatory adherence
- 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 retraining
- Drive adoption of emerging AI technologies and establish best practices
- Mentor ML engineers
- Shape AI/ML roadmap and establish center of excellence for compliance AI
- Collaborate with product leadership on long-term vision for AI-powered compliance
- * Interested candidates can share their updated resumes at [HIDDEN TEXT]
Qualifications
- Location – Kolkata/ Indore/ Bangalore/ Pune
- Notice Period – Immediate joiners only
- 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.
- 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 (Elasticsearch, 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 Bedrock, Bedrock AgentCore Runtime, SageMaker, 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
- 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)
- Understanding of ML governance, bias detection, model risk management, and data privacy regulations (GDPR, CCPA, HIPAA)
- AWS ML certifications or similar credentials
- Strong communication skills explaining complex models to technical and non-technical 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