Company
SeniorMachineLearningEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Machine Learning Engineer. Skills: Agentic AI, LLM Fine-Tuning, Prompt Engineering, Generative AI. Design, develop, and optimize domain adaptive agentic AI. Fine-tune large-scale pre-trained models”
Industry & Context.
problem-solving; analytical skills
What They're Looking For.
Must Have
3 to 5 years of hands-on 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, Experience in prompt engineering to optimize AI models performance, Proficiency in Python, Proficiency in TensorFlow, Proficiency in PyTorch, Proficiency in 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, Knowledge of reinforcement learning, Knowledge of multi-agent systems, Knowledge of other autonomous decision-making frameworks, Familiarity with SDLC life cycle, Familiarity with data processing tools, Familiarity with version control (Git)
Nice to Have
Experience in deploying AI models at scale in production environments, Expertise in large-scale data processing, Expertise in optimization techniques, Expertise in model deployment
What You'll Do.
and optimize domain adaptive agentic AI
Fine-tune large-scale pre-trained models
Adapt models for specific applications and domains
Evaluate and optimize models for performance
Design prompts with techniques like Chain of Thought
Ensure model outputs are aligned with use case
Build end-to-end workflows for AI solutions
Collect and preprocess data
Continuously improve AI models in production environments
Collaborate with data scientists
Define AI product requirements
Deliver innovative solutions
Stay current with the latest research and developments
Deploy machine learning models at scale
Optimize models for latency
Maintain clear documentation of models
Communicate results effectively to stakeholders
How You'll Work.
Team & Collaboration
Cross-functional Teams; Data scientists; Software engineers; Product managers
Communication Scope
Communicate results effectively
Full Job Description
While technology is the heart of our business, a global and diverse culture is the heart of our success. We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth. If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi! Role : Senior Machine Learning Engineer Experience Level : 3 to 6 years Roles & Responsibilities: ● Agentic AI Development: Design, develop, and optimize domain adaptive agentic AI systems that helps in automating business processes ● LLM Fine-Tuning: 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. ● Prompt Engineering: Design prompts with techniques like Chain of Thought, Few Shot to enhance model responses, ensuring that model outputs are aligned with use case requirements. ● AI Workflow Automation: Build end-to-end workflows for AI solutions, from data collection and preprocessing to training, deployment, and continuous improvement in production environments. ● Collaboration with Cross-functional Teams: Work closely with data scientists, software engineers, and product managers to define AI product requirements and deliver innovative solutions. ● Research & Development: 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. ● Scaling and Deployment: Deploy machine learning models at scale, optimizing for latency, throughput, and robustness in production environments. ● Documentation & Reporting: Maintain clear documentation of models, workflows, and experiments, and communicate results effectively to stakeholders. Required Skills
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