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Role Description
This position requires expertise in designing, developing, debugging, and maintaining AI-powered applications and data engineering workflows for both local and cloud environments.
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Work on large-scale projects, optimizing AI/ML pipelines, and ensuring scalable data infrastructure.
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Integrate Generative AI (GenAI) capabilities, build data pipelines for AI model training, and deploy scalable AI-powered microservices.
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Collaborate with AI/ML, Data Engineering, DevOps, and Product teams to deliver impactful solutions that enhance products and services.
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Desirable experience in retrieval-augmented generation (RAG), fine-tuning pre-trained LLMs, AI model evaluation, data pipeline automation, and optimizing cloud-based AI deployments.
Responsibilities
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AI-Powered Software Development & API Integration:
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Develop AI-driven applications, microservices, and automation workflows using FastAPI, Flask, or Django, ensuring cloud-native deployment and performance optimization.
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Integrate OpenAI APIs (GPT models, Embeddings, Function Calling) and Retrieval-Augmented Generation (RAG) techniques to enhance AI-powered document retrieval, classification, and decision-making.
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Data Engineering & AI Model Performance Optimization:
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Design, build, and optimize scalable data pipelines for AI/ML workflows using Pandas, PySpark, and Dask, integrating data sources such as Kafka, AWS S3, Azure Data Lake, and Snowflake.
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Enhance AI model inference efficiency by implementing vector retrieval using FAISS, Pinecone, or ChromaDB, and optimize API latency with tuning techniques (temperature, top-k sampling, max tokens settings).
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Microservices, APIs & Security:
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Develop scalable RESTful APIs for AI models and data services, ensuring integration with internal and external systems while securing API endpoints using OAuth, JWT, and API Key Authentication.
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Implement AI-powered logging, observability, and monitoring to track data pipelines, model drift, and inference accuracy, ensuring compliance with AI governance and security best practices.
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AI & Data Engineering Collaboration:
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Work with AI/ML, Data Engineering, and DevOps teams to optimize AI model deployments, data pipelines, and real-time/batch processing for AI-driven solutions.
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Engage in Agile ceremonies, backlog refinement, and collaborative problem-solving to scale AI-powered workflows in areas like fraud detection, claims processing, and intelligent automation.
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Cross-Functional Coordination and Communication:
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Collaborate with Product, UX, and Compliance teams to align AI-powered features with user needs, security policies, and regulatory frameworks (HIPAA, GDPR, SOC2).
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Ensure seamless integration of structured and unstructured data sources (SQL, NoSQL, vector databases) to improve AI model accuracy and retrieval efficiency.
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Mentorship & Knowledge Sharing:
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Mentor junior engineers on AI model integration, API development, and scalable data engineering best practices, and conduct knowledge-sharing sessions.
Qualifications
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12-18 years of experience in software engineering or AI/ML development, preferably in AI-driven solutions.
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Hands-on experience with Agile development, SDLC, CI/CD pipelines, and AI model deployment lifecycles.
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Bachelor’s Degree or equivalent in Computer Science, Engineering, Data Science, or a related field.
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Proficiency in full-stack development with expertise in Python (preferred for AI), Java.
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Experience with structured & unstructured data:
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SQL (PostgreSQL, MySQL, SQL Server)
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NoSQL (OpenSearch, Redis, Elasticsearch)
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Vector Databases (FAISS, Pinecone, ChromaDB)
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Cloud & AI Infrastructure:
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AWS: Lambda, SageMaker, ECS, S3
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Azure: Azure OpenAI, ML Studio
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GenAI Frameworks & Tools: OpenAI API, Hugging Face Transformers, LangChain, LlamaIndex, AutoGPT, CrewAI.
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Experience in LLM deployment, retrieval-augmented generation (RAG), and AI search optimization.
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Proficiency in AI model evaluation (BLEU, ROUGE, BERT Score, cosine similarity) and responsible AI deployment.
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Strong problem-solving skills, AI ethics awareness, and the ability to collaborate across AI, DevOps, and data engineering teams.
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Curiosity and eagerness to explore new AI models, tools, and best practices for scalable GenAI adoption.
Benefits
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Health and financial benefits.
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Perks specific to each location, including commuter support, employee assistance programs, tuition assistance, employee resource groups, and collaborative workspaces.
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Events throughout the year, including book clubs, external speakers, and hackathons.
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Company culture based on learning, support of an engaged team, and an inclusive environment where all employees are valued.
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Encouragement of a better work-life balance with flexibility in work arrangements.