We are looking for a highly skilled AI Engineer with deep expertise across machine learning frameworks, large language models, Information Retrieval, and AI systems integration—particularly within life sciences and pharmaceutical applications. The AI Engineering Engineer will work closely with our AI engineering team to support the development, testing, and deployment of AI/ML solutions. This role provides a unique opportunity to gain hands-on experience with cutting-edge technologies and contribute to real-world projects that drive innovation within our organization.Programming & Frameworks: Proficient in Python with extensive hands-on experience in machine learning frameworks such as PyTorch, TensorFlow, and related libraries. LLMs & Fine-Tuning: Expertise in large and small language models such as GPTs, Mistral, LLaMA, DeepSeek, Phi3, and etc. Skilled in fine-tuning techniques like PEFT/LoRA. Familiar with Reinforcement Learning with or without Human Feedback (RLHF, GRPO, etc.). Familiar with model distilling techniques. ElasticSearch & Knowledge Retrieval Systems: Proficiency in Advanced Retrieval-Augmented Generation (RAG) methodologies. Hands-on experience with ElasticSearch and Elastic Stack for search optimization, vector databases like Chroma or Pinecone, and knowledge graph tools such as Neo4j. Agent Building: Strong understanding of agentic frameworks. Capable of designing intelligent agents with dynamic reasoning capabilities. Familiarity with agent frameworks such as LangChain, LangGraph, Model Context Protocol (MCP) for building autonomous AI agents. MLOps Expertise: Proficiency in MLOps practices, including CI/CD pipelines, model monitoring, and optimization. Experience in deploying scalable machine learning pipelines to production environments. Kubernetes & Cloud Deployment: Skilled in deploying containerized machine learning models using Kubernetes for orchestration. Expertise in tools like Kubeflow for end-to-end ML workflow management and cloud platforms (AWS/Azure/GCP) for scalable infrastructure.

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, with 5+ years of experience in machine learning or AI engineering field. Pharma Domain Knowledge: Experience in applying AI/ML solutions within the pharmaceutical domain is highly desirable. Familiarity with USDM standards, CDISC, tools like cTAKES for medical text processing, and OpenStudyBuilder for clinical trial design is a strong advantage, is a big plus. Proven ability to design scalable machine learning systems and integrate them into production environments. Strong background in data analysis and preprocessing for model optimization. Experience working with distributed systems to enhance model training efficiency using GPUs or TPUs. Familiarity with ML observability tools tools, e.g. Weights and Bias, MLflow, for debugging and improving AI models in real-time.