Sr. AI Engineer

Call for Applications for Sr. AI Engineer (R -07) – ANNAM.AI Location: ANNAM.AI, Center of Excellence in AI for Agriculture, IIT Ropar (Onsite preferred; hybrid/remote may also be considered) Type: Full-Time | Contractual | Renewable based on performance Compensation: ₹15 - 25 Lakhs per annum (Based on

qualifications

and experience) Performance Incentives: Additional incentives based on performance and project outcomes, subject to Governing Board approval Tenure: Initial appointment for 2 years, extendable up to 5 years based on performance Minimum

experience

of 3 years (May be relaxed in the case of exceptional candidates.) Role Summary As a Sr. AI Engineer at ANNAM.AI, you will lead the design, development, and deployment of advanced AI-driven solutions to transform agriculture, focusing on areas such as precision farming, crop monitoring, soil health analysis, and farmer enablement. You will spearhead high-impact projects, mentor junior engineers, and collaborate with agricultural experts to integrate AI/ML and Generative AI technologies into India’s farming ecosystem. This role is ideal for an experienced AI professional with a passion for leveraging cutting-edge technologies like LLMs, RAG, and Transformers to address real-world agricultural challenges and drive sustainable, scalable impact.

Key Responsibilities

AI Model Development and Optimization Lead the design and development of advanced machine learning 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.

Candidate Profile

Educational

Qualifications

&

Skills

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. Professional

Experience

Proven Track in in AI/ML development, with

experience

in Sr. technical roles Proven track record in designing and deploying AI solutions, prefera

and experience) Performance Incentives: Additional incentives based on performance and project outcomes, subject to Governing Board approval Tenure: Initial appointment for 2 years, extendable up to 5 years based on performance Minimum

experience

of 3 years (May be relaxed in the case of exceptional candidates.) Role Summary As a Sr. AI Engineer at ANNAM.AI, you will lead the design, development, and deployment of advanced AI-driven solutions to transform agriculture, focusing on areas such as precision farming, crop monitoring, soil health analysis, and farmer enablement. You will spearhead high-impact projects, mentor junior engineers, and collaborate with agricultural experts to integrate AI/ML and Generative AI technologies into India’s farming ecosystem. This role is ideal for an experienced AI professional with a passion for leveraging cutting-edge technologies like LLMs, RAG, and Transformers to address real-world agricultural challenges and drive sustainable, scalable impact.

Key Responsibilities

AI Model Development and Optimization Lead the design and development of advanced machine learning 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