GenAI Software Engineer
Remote
Full Time
Information Technology
Experienced
Role: GenAI Software Engineer
Level: Mid to Senior
Work hours: 2:30 PM IST to 10:30 PM IST
Role Type: Remote/WFH, India
Full Time/Permanent Hire
Qualifications:
Responsibilities
Level: Mid to Senior
Work hours: 2:30 PM IST to 10:30 PM IST
Role Type: Remote/WFH, India
Full Time/Permanent Hire
Qualifications:
- Experience Requirements: 8-10 years of experience in developing Data Science, AI, and ML solutions, with a specific focus on generative AI and LLMs in the Finance/Banking domain.
- Post/Graduate degree in information technology, Banking domain experience required, Computer Science, Artificial Intelligence, or a related field.
- Prior experience in identifying new opportunities to optimize the business through analytics, AI/ML and use case prioritization.
- The individual should be a thought leader having a well-balanced analytical business acumen, domain, and technical expertise.
- Large Language Model Expertise: Experience in working with and fine-tuning Large Language Models (LLMs), including the design, optimization of NLP systems, frameworks, and tools.
- Application Development with LLMs: Experience in building scalable applications using LLMs, utilizing frameworks such as LangChain, LlamaIndex, etc and productionizing machine learning and AI models.
- Skills required for Vertex AI, Knowledge of GCP platform is must.
- Language Model Development: Utilize off-the-shelf LLM services, such as Azure OpenAI, Vertex AI, to integrate LLM capabilities into applications.
- Cloud Computing Expertise: Proven architect kind of experience in cloud computing, particularly with Azure Cloud Services.
- Technical Proficiency: Strong skills in UNIX/Linux environments and command-line tools
- Programming and ML Skills: Proficiency in Python, with a deep understanding of machine learning algorithms, deep learning, and generative models.
- Advanced AI Skills and Testing: Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch), hands-on experience in deploying AI/ML solutions as a service/REST API on Cloud or Kubernetes, and proficiency in testing of developed AI components.
Responsibilities
- In this role, you will ensure that software is developed to meet functional, non-functional and compliance requirements, and solutions are well designed with maintainability/ease of integration and testing built-in from the outset.
- Hands-on engagement in the full software lifecycle activities is expected, this includes requirements analysis, architecture design, coding, testing, and deployment.
- Job expectations include a solid knowledge of development and testing practices common to the industry and design and architectural patterns.
- Code solutions and implement automated unit tests to deliver a requirement/story per the defined acceptance criteria and compliance requirements.
- Design, develop, and modify architecture components, application interfaces, and solution enablers while ensuring principal architecture integrity is maintained.
- Mentor other software engineers and coach team on Continuous Integration and Continuous Development (CI-CD) practices and automating tool stack.
- Execute story refinement, definition of requirements, and estimating work necessary to realize a story through the delivery lifecycle.
- Perform proof of concept as necessary to implement new ideas or mitigate risk
- Automate manual release activities.
- Develop and maintain scalable data ingestion pipelines from various data sources, including large databases (Hadoop, etc.), web services, and APIs.
- Analyze and organize raw data from different sources: internal or external.
- Implement data cleaning, transformation, and normalization processes to ensure data quality and consistency.
- Implement automated unit tests and conduct reviews with other team members to make sure your code is rigorously designed, elegantly coded, and effectively tuned for performance.
- Automate reports and processes to run with varying frequencies.
- Monitor and troubleshoot data pipelines to ensure data accuracy and pipeline performance.
- Explore ways to enhance data quality, reliability, and efficiency of data pipelines.
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