3 Days Left Junior Engineer: AI/ML R&D

Role: AI ML Engineer(R&D)

Mode of Work: Work from Office

Employment Type: Full-time

Exp: 1 - 3 Years

Location: Trivandrum

Company Description

Litmus7 is a Retail Maximization company that specializes in accelerating digital revenue and profitability for Retailers, Brands, and CPGs. Our services include solutions, products, and concepts that are essential for a retailer's survival in this fast-changing consumer industry, coupled with Technology services to stabilize and scale their digital ecosystem. We're rated No.1 in credibility and work with the top 5 global retailers in Grocery, Fashion, Beauty, Pet Care, and General Merchandise. Our 700+ specialist technologists at six worldwide centers across USA, India, Canada, Australia, and UAE are passionate creators and problem-solvers.

Role Description

We are on the hunt for an extraordinary Senior AI/ML Engineer who harbors a deep-seated passion for artificial intelligence. This is the perfect playground for a visionary who thrives on overcoming challenges—not just facing them—who tackles problem-solving with a unique blend of creative flair and meticulous attention to detail. As the vanguard of our technical team, you'll be a critical contributor in the creation of cutting-edge AI models, hone their performance to perfection, and masterfully translate complex theoretical constructs into robust, production-caliber innovations.

- Craft, refine, troubleshoot, and launch sophisticated predictive models, deep learning algorithms, and machine learning frameworks.
- Unravel the intricate web of ML algorithms, their structures, features, and any pesky bugs, identifying root causes and design intelligent solutions.
- Scout for breakthrough opportunities to inject ingenuity, expedite product debuts, and dismantle impediments, all in the name of driving dynamic results.
- Delve into the depths of data to distil root issues, craft detailed reports, and detect recurring problem patterns through sharp analysis.
- Design and develop a large language model (LLM) use case, ensuring it is equipped with adequate guardrails and leverages advanced LLM Operations techniques to guarantee a high-quality output upon production.
- Train and retrain systems as needed, incorporating adaptability and foresight into the continuous improvement process.
- Conduct thorough literature reviews, staying at the forefront of the latest developments in the specified research areas.
- Analyze and interpret research findings, actively contributing to the publication of groundbreaking research papers in prestigious conferences and journals.
- Engage proactively in regular team meetings to share progress, address challenges, and brainstorm potential improvements.

Qualifications

- At least 2years in AI/ML R&D backed by an M.Tech or Ph.D., with a track record of contributions to top-tier conferences, journals, or patents.
- Prior exposure to the Retail Domain is a sterling advantage.
- Proficient coding skills in Python, Julia, or RUST.
- Comprehensive expertise in developing and implementing deep learning architectures and classical machine learning models with MLOPS techniques.
- Having experience with large language models (LLMs) and successfully integrating them into products through robust LLM Operations would be highly advantageous.
- A solid grasp of Data Structures, Algorithms, and Complexity Analysis.
- Exemplary attention to detail, coupled with strong problem-solving, analytical, and organizational talents.
- Proficient in the application of CUDA and cuDNN libraries for high-performance computing in AI.
- Adept at conveying complex concepts to a varied audience, bridging the gap between technical and non-technical collaborators.
- Well-versed in data science methodologies and frameworks.
- A self-starter with an insatiable curiosity and a penchant for pushing the envelope in R&D pursuits.
- Fair understanding in design and implementation of deep learning architectures / classical ML Models.

Areas of Research (going on):

- Graph Self-Supervised Learning
- Temporal Graph Learning.
- Multi-modal graph representation learning
- HIIL DRL Techniques for conversational intelligence
- Mechanistic interpretation of LLMs
- Topological Deep Learning
- Cognitive Assessment using AI Techniques
- Autonomous Knowledge Graphs
- Neuro-symbolic AI
- AGI
- Federated Graph Neural Networks

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