GE
GetinzAI/ML Engineer
Innovation Engineer responsible for AI - driven solutions at a digital commerce company. Focused on prototyping, exploring technologies, and shaping technology strategy.
About the role
- Design, develop, and optimize domain adaptive agentic AI systems that helps in automating business processes
- Work with large-scale pre-trained models (like Llama, Mistral etc.) to fine-tune with techniques like PEFT, SFT and adapt them for specific applications and domains.
- Evaluate and Optimize for performance, accuracy, and efficiency.
- Design prompts with techniques like Chain of Thought, Few Shot to enhance model responses, ensuring that model outputs are aligned with use case requirements.
- Build end-to-end workflows for AI solutions, from data collection and preprocessing to training, deployment, and continuous improvement in production environments.
- Work closely with data scientists, software engineers, and product managers to define AI product requirements and deliver innovative solutions.
- Stay current with the latest research and developments in generative AI, deep learning, NLP, reinforcement learning, and related fields to ensure that the organization stays at the forefront of technology.
- Deploy machine learning models at scale, optimizing for latency, throughput, and robustness in production environments.
- Maintain clear documentation of models, workflows, and experiments, and communicate results effectively to stakeholders.
Requirements
- 6 to 9 years of work experience in machine learning and AI engineering.
- Proven track record in working with LLMs such as Llama, Mistral and models like GPT, BERT, T5, or similar.
- Expertise in designing, fine-tuning, and deploying generative AI models and building agentic workflows.
- Strong experience in prompt engineering to optimize AI models performance.
- Technical Skills - Proficiency in Python, TensorFlow, PyTorch, or other ML frameworks.
- Proficiency in building agentic workflows with tools like Langgraph, CrewAI, Autogen, PhiData or similar.
- Familiarity with cloud platforms (AWS, GCP, Azure) for deployment and scaling of models.
- Experience with NLP tasks, such as text classification, text generation, summarization, and question answering.
- Knowledge of reinforcement learning, multi-agent systems, or other autonomous decision-making frameworks.
- Familiarity with SDLC life cycle, data processing tools (e.g., Pandas, NumPy, etc.) and version control (e.g., Git).
Benefits
- Competitive salary
- Flexible working hours
- Professional development opportunities
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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