Senior AI/ML Engineer
3 weeks ago
Role: Senior AI/ML Engineer(R&D)
Mode of Work: Work from Office
Employment Type: Full-time
Exp: 4 - 6 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
- Design, develop, debug and deploy predictive and deep learning, machine learning algorithms.
- Dive into the technical complexity of ML algorithms, architectures, features and bugs when needed and be able to recognize root cause and recognize solutions.
- Dive deep to identify root causes of issues and/or generate reports.
- Identify opportunities to drive innovation, accelerate product launches, and remove bottlenecks to deliver results and extract data to identify problem patterns.
Qualifications
- 4+ years of AI/ML R&D experience with M Tech or Ph.D., preferably having publications in premium conferences/journals/patents.
- Experience working with Retail Domain, providers is a plus.
- Strong programming experience in Python/R/Julia.
- Thorough understanding in design and implementation of deep learning architectures / classical ML Models.
- Strong knowledge on Data Structures, Algorithms, Complexity Analysis etc.
- Strong attention to detail, problem solving, analytical and organizational skills.
- Ability to communicate effectively with both technical and non-technical individuals.
- Familiarity with data science tools and frameworks.
- Passionate individual with self-motivation for going beyond the capabilities to deliver niche R&D output.
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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