Associate Manager Software Engineering

Job Description:

We are looking for a talented and motivated AI/ML & GenAI Engineer with an experience of 8 - 12 years of total IT experience and not less than 5 years of exposure to real-time AI based projects (Any academic or professionally accreditations in the AI space will be extremely nice to have) to join our innovative team and drive the development of intelligent solutions that solve complex problems. In this role, you will design, build, and deploy machine learning models and AI systems that integrate seamlessly into our products and services.

You will collaborate with cross-functional teams, including data scientists, software engineers, and product managers, to identify opportunities where AI/ML & GenAI solutions can deliver value. This role offers the chance to work on cutting-edge technologies, develop scalable algorithms, and contribute to shaping the future of AI-powered solutions.

Data Preparation & OCR Integration: Clean and preprocess raw data, implement like AWS Textract for high‑accuracy document extraction, and seamlessly integrate outputs into downstream ETL pipelines.

NLP Development: Build, fine‑tune, and deploy NLP models for domain‑specific tasks such as proposal analysis and financial text processing.

AI/ML Model Development: Architect, train, and productionize machine‑learning models to assess profitability, classify proposals, and deliver automated recommendations.

Generative AI & LLM Engineering: Design and implement retrieval‑augmented generation (RAG) pipelines, integrate large language models (e.g., AWS Bedrock, LLaMA) with frameworks like LangChain, and refine prompt‑engineering strategies for precise, domain‑specific content generation.

Deployment & Monitoring: Deploy AI/ML and Generative AI solutions on platforms like AWS SageMaker, establish real‑time monitoring and alerting, and ensure horizontal scalability.

System Integration & Optimization: Embed AI/ML components into existing workflows, optimize for low latency and resource efficiency, and maintain robust error‑handling.

Research & Experimentation: Stay abreast of emerging AI/ML and Generative AI innovations, conduct experiments with state‑of‑the‑art algorithms, and perform A/B testing to validate model performance.

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