Sr Associate Software Engineer

Roles & Responsibilities

• Search Algorithm Design and Implementation:
• Design and implement search algorithms using NLP, machine learning, semantic understanding, and deep learning models.
• Build and fine-tune models for information retrieval, query expansion, document ranking, summarization, and Q&A systems.
• Support integration of LLMs (e.g., GPT, BERT, Bio BERT) for semantic and generative search.
• Train and evaluate custom models for biomedical named entity recognition, relevance ranking, and similarity search.
• Build and deploy vector-based search systems using embeddings and vector databases.
• AI Model Integration and Deployment:
• Work closely with platform engineers to integrate AI models into scalable cloud-based infrastructures (AWS, Azure, GCP).
• Package and deploy search services using containerization (Docker, Kubernetes) and modern MLOps pipelines.
• Content Processing and Enrichment:
• Preprocess and structure unstructured content such as clinical trial reports, research articles, and regulatory documents.
• Apply knowledge graphs, taxonomies, and ontologies (e.g., MeSH, UMLS, SNOMED) to enhance search results.
• Recommendation Systems and Generative AI:
• Build and deploy recommendation systems models, utilize AIML infrastructure, and contribute to model optimization and data processing.
• Gain experience in Generative AI on Search Engines.
• Experience integrating Generative AI capabilities and Vision Models to enrich content quality and user engagement.
• Experience with Generative AI tasks such as content summarization, deduping, and metadata quality.
• Researching and developing advanced AI algorithms, including Vision Models for visual content analysis.
• Performance and Continuous Improvement:
• Implement KPI measurement frameworks to evaluate the quality and performance of delivered models, including those utilizing Generative AI.
• Developing and maintaining Deep Learning models for data quality checks, visual similarity scoring, and content tagging.
• Continually researching current and emerging technologies and proposing changes where needed.
• Implement GenAI solutions, utilize ML infrastructure, and contribute to data preparation, optimization, and performance enhancements.

Basic Qualifications

• Education and Experience:
• Degree in computer science & engineering preferred with six to eight years of software development experience.
• Two to four years of experience building AI/ML models, ideally in search, NLP, or biomedical domains.
• Technical Proficiency:
• Proficiency in Python and frameworks such as PyTorch, TensorFlow, Hugging Face Transformers.
• Experience with search technologies like Elasticsearch, OpenSearch, or vector search tools.
• Solid understanding of NLP techniques: embeddings, transformers, entity recognition, text classification.
• Hands-on experience with various AI models, GCP Search Engines, and GCP Cloud services.
• Proficient in programming languages like AI/ML, Python, Java Crawlers, JavaScript, SQL/NoSQL.
• Experience with Databricks/RDS, S3 Buckets, DynamoDB.
• Problem-Solving and Communication:
• Strong problem-solving and analytical skills.
• Ability to learn quickly.
• Excellent communication and interpersonal skills.

Preferred Qualifications

• Programming and Platforms:
• Experience in AI/ML, Java, Python.
• Experienced with Fast Python API, GraphQL.
• Experience with design patterns, data structures, data modeling, data algorithms.
• Experienced with AWS/Azure Platform, including building and deploying code.
• Experience in Postgres SQL/Mongo DB SQL database, vector database for large language models, Databricks or RDS, S3 Buckets.
• AI and NLP:
• Knowledge of LLMs, generative AI, and their use in enterprise search.
• Experience in Google Cloud Search and Google Cloud Storage.
• Experience with popular large language models.
• Experience with LangChain or LlamaIndex framework for language models.
• Experience with prompt engineering, model fine-tuning.
• Knowledge of NLP techniques for text analysis and sentiment analysis.
• Development Methodologies:
• Experience in Agile software development methodologies.
• Experience in End-to-End testing as part of Test-Driven Development.

Good-to-Have Skills

• Willingness to work on Full Stack Applications.
• Exposure to MLOps tools like MLflow, Airflow, or SageMaker.

Soft Skills

• Excellent analytical and troubleshooting skills.
• Strong verbal and written communication skills.
• Ability to work effectively with global, remote teams.
• High degree of initiative and self-motivation.
• Ability to manage multiple priorities successfully.
• Team-oriented, with a focus on achieving team goals.
• Strong presentation and public speaking skills.