Sr. AI Engineer

AI Model Development and Optimization

Lead the design and development of advanced machine learnin g and Generative AI models for applications like crop yield prediction, pest detection, and irrigation optimization.

Develop and fine-tune Large Language Models (LLMs) and implement Retrieval-Augmented Generation (RAG) systems for farmer advisory solutions and decision support tools.

Optimize AI models for performance, scalability, and deployment in resource-constrained rural environments.

Oversee data preprocessing, feature engineering, and integration of diverse datasets (e.g., satellite imagery, IoT sensor data, weather data).

Work with transformer-based architectures such as BERT, GPT, and LLaMA to power advanced agricultural applications.

Technology Deployment and Scalability

Drive the deployment of AI tools and platforms in rural agricultural settings, ensuring accessibility and usability for farmers and field practitioners.

Build scalable solutions using LangChain , Vector Databases , and cloud platforms (AWS, Azure, GCP).

Lead pilot projects and proof-of-concept initiatives to validate AI solutions in real-world farming scenarios.

Collaboration and Leadership

Collaborate with interdisciplinary teams, including agricultural scientists, data analysts, and external stakeholders (e.g., ICAR, FPOs, startups), to align AI solutions with agricultural needs.

Mentor and guide junior AI engineers, fostering a culture of innovation and technical excellence within ANNAM.AI.

Represent ANNAM.AI in technical discussions, workshops, and stakeholder meetings to promote AI-driven agricultural innovation.

Research and Innovation

Conduct research to explore state-of-the-art AI and Generative AI applications in agriculture, such as: Computer vision for crop health monitoring, LLM-based farmer advisory chatbots, RAG-based information systems.​

Create case studies to showcase ANNAM.AI’s advancements in agri-tech.

Stay updated on emerging AI/ML trends and integrate relevant advancements into ongoing projects.

Stakeholder Engagement and Policy Support

Engage with government bodies (e.g., MoA&FW, NABARD) and agri-tech partners to support the adoption of AI solutions.

Provide technical inputs for policy recommendations on AI in agriculture, focusing on sustainability and farmer welfare.

Contribute to capacity-building initiatives, such as training programs or hackathons, for students and innovators.

Bachelor’s, Master’s or PhD degree in Computer Science, Artificial Intelligence, Data Science, or a related discipline from a reputed institution.

Expertise in AI/ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn) and programming languages (e.g., Python, C++, Java).

Strong experience with transformer architectures (BERT, GPT, LLaMA) and Generative AI .

Proficiency in Prompt Engineering , RAG pipelines , and building systems using LangChain and Vector Databases .

Strong knowledge of advanced AI techniques, such as deep learning, computer vision, or reinforcement learning, with applications in agriculture or similar domains.

Familiarity with cloud platforms (e.g., AWS, Azure, GCP), IoT systems, or big data technologies is highly desirable.

Proven Track in in AI/ML development, with experience in Sr. technical roles

Proven track record in designing and deploying AI solutions, preferably in agri-tech, healthcare, or rural technology ecosystems.

Experience collaborating with interdisciplinary teams or external stakeholders (e.g., government bodies, startups, or research institutes) is an advantage.

Prior involvement in publishing research or leading high-impact AI projects is preferred.